CentroidHomfold-LAST: accurate prediction of RNA secondary structure using automatically collected homologous sequences

RNA nanotechnology on the horizon: Self-assembly, chemical modifications, and functional applications

RNA nanotechnology harnesses the unique chemical and structural properties of RNA to build nanoassemblies and supramolecular structures with dynamic and functional capabilities. This review focuses on design and assembly approaches to building RNA structures, the RNA chemical modifications used to enhance stability and functionality, and modern-day applications in therapeutics, biosensing, and bioimaging.

Analysis on the in-silico ensemble methods for 3D modelling of ssDNA aptamers

Understanding the 3-D structure of nucleic acid aptamers is important for the rational design of aptamer-based constructs in various applications, including for developing aptasensors. Herein, a simple approach for 3D modelling of ssDNA aptamers through an ensemble of web applications has been described. The procedure utilized 30 aptamers whose 3D XRD or NMR experimental structures are available for validation. As a first step, the primary sequences of ssDNA aptamers were transformed into 2D structures using six widely used web applications: RNA fold, Vector builder, RNA Structure, UNA fold, Centroid fold, and IP Knot. The generated 2D structures were then passed through the RNA composer web application to generate 3D RNA structure, which in turn was converted to 3D DNA structures using various Visual Molecular Dynamics web applications that also include conversion of ribose sugar into deoxyribose sugar backbone and uracil to thiamine. The energy-minimized generated 3D structures were matched well with high accuracy to their experimental counterparts. This study identified that the Guanine residues are crucial in the aptamer 3D structure prediction and in algorithms that generate secondary structures. Further, the GC content (<50%), GC bond percentage (<60%), and G:C ratio (<1.12) act as limiting factors in predicting the 2D structures of aptamers. There were variations in the 2D structure predictions by the web applications, even though all these applications were a combination of the MFE, MEA, and McCaskill functions. Processing these structures through the web applications described above produced best-fit 3D structures with the experimental one, thus offering the present ensemble approach to reliably predict the 3D structure of aptamers for various applications.

Comparative analysis of RNA secondary structure accuracy on predicted RNA 3D models

RNA structure is conformationally dynamic, and accurate all-atom tertiary (3D) structure modeling of RNA remains challenging with the prevailing tools. Secondary structure (2D) information is the standard prerequisite for most RNA 3D modeling. Despite several 2D and 3D structure prediction tools proposed in recent years, one of the challenges is to choose the best combination for accurate RNA 3D structure prediction. Here, we benchmarked seven small RNA PDB structures (40 to 90 nucleotides) with different topologies to understand the effects of different 2D structure predictions on the accuracy of 3D modeling. The current study explores the blind challenge of 2D to 3D conversions and highlights the performances of de novo RNA 3D modeling from their predicted 2D structure constraints. Our results show that conformational sampling-based methods such as SimRNA and IsRNA1 depend less on 2D accuracy, whereas motif-based methods account for 2D evidence. Our observations illustrate the disparities in available 3D and 2D prediction methods and may further offer insights into developing topology-specific or family-specific RNA structure prediction pipelines.

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.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

Genomic Characterization of Escherichia coli Isolates Belonging to a New Hybrid aEPEC/ExPEC Pathotype O153:H10-A-ST10 eae-beta1 Occurred in Meat, Poultry, Wildlife and Human Diarrheagenic Samples

Different surveillance studies (2005–2015) in northwest Spain revealed the presence of eae-positive isolates of Escherichia coli O153:H10 in meat for human consumption, poultry farm, wildlife and human diarrheagenic samples. The aim of this study was to explore the genetic and genomic relatedness between human and animal/meat isolates, as well as the mechanism of its persistence. We also wanted to know whether it was a geographically restricted lineage, or whether it was also reported elsewhere. Conventional typing showed that 32 isolates were O153:H10-A-ST10 fimH54, fimAv_MT78, traT and eae-beta1. Amongst these, 21 were CTX-M-32 or SHV-12 producers. The PFGE XbaI-macrorestriction comparison showed high similarity (>85%). The plasmidome analysis revealed a stable combination of IncF (F2:A-:B-), IncI1 (STunknown) and IncX1 plasmid types, together with non-conjugative Col-like plasmids. The core genome investigation based on the cgMLST scheme from EnteroBase proved close relatedness between isolates of human and animal origin. Our results demonstrate that a hybrid MDR aEPEC/ExPEC of the clonal group O153:H10-A-ST10 (CH11-54) is circulating in our region within different hosts, including wildlife. It seems implicated in human diarrhea via meat transmission, and in the spreading of ESBL genes (mainly of CTX-M-32 type). We found genomic evidence of a related hybrid aEPEC/ExPEC in at least one other country.

Rtools: a web server for various secondary structural analyses on single RNA sequences

The secondary structures, as well as the nucleotide sequences, are the important features of RNA molecules to characterize their functions. According to the thermodynamic model, however, the probability of any secondary structure is very small. As a consequence, any tool to predict the secondary structures of RNAs has limited accuracy. On the other hand, there are a few tools to compensate the imperfect predictions by calculating and visualizing the secondary structural information from RNA sequences. It is desirable to obtain the rich information from those tools through a friendly interface. We implemented a web server of the tools to predict secondary structures and to calculate various structural features based on the energy models of secondary structures. By just giving an RNA sequence to the web server, the user can get the different types of solutions of the secondary structures, the marginal probabilities such as base-paring probabilities, loop probabilities and accessibilities of the local bases, the energy changes by arbitrary base mutations as well as the measures for validations of the predicted secondary structures. The web server is available at http://rtools.cbrc.jp, which integrates software tools, CentroidFold, CentroidHomfold, IPKnot, CapR, Raccess, Rchange and RintD.

Secondary structure conservation of the stem-loop IV sub-domain of internal ribosomal entry sites in human rhinovirus clinical isolates

OBJECTIVES

The aim of this study was to investigate the genetic diversity in the stem-loop (SL) IV sub-domain of the human rhinovirus (HRV) internal ribosomal entry site (IRES), which plays key roles in the initiation of viral translation by host protein interaction.

METHODS

The primary SL-IV sequences of 194 HRVs, consisting of 97 reference strains and 97 clinical isolates, including the IRES sub-domains SL-IVa, SL-IVb, SL-IVc, and SL-IVd, were analyzed using Lasergene, MEGA 4, and WebLogo. Additionally, secondary structures of SL-IV were predicted and classified by RNAfold and CentroidHomfold-LAST.

RESULTS

The predicted secondary structures of SL-IV showed variations in the position of bulbs, size of the loop, and length of stems. SL-IVc had the most highly conserved nucleotide sequence, with structures classified into two groups by the location of the poly(C) loop. Of the SL-IV sequences analyzed, 74 (79.56%) were classified in the major group and 19 (20.44%) in the minor group. Thirteen compensatory substitution pairs of SL-IVc contributed to maintaining the stem structure.

CONCLUSIONS

This study showed that the IRES secondary structures of a large number of reference and clinical HRVs were highly conserved, with several compensatory substitutions. It is expected that these results will facilitate investigations into HRV function based on IRES secondary structures.

A general approach to high-yield biosynthesis of chimeric RNAs bearing various types of functional small RNAs for broad applications

RNA research and therapy relies primarily on synthetic RNAs. We employed recombinant RNA technology toward large-scale production of pre-miRNA agents in bacteria, but found the majority of target RNAs were not or negligibly expressed. We thus developed a novel strategy to achieve consistent high-yield biosynthesis of chimeric RNAs carrying various small RNAs (e.g. miRNAs, siRNAs and RNA aptamers), which was based upon an optimal noncoding RNA scaffold (OnRS) derived from tRNA fusion pre-miR-34a (tRNA/mir-34a). Multi-milligrams of chimeric RNAs (e.g. OnRS/miR-124, OnRS/GFP-siRNA, OnRS/Neg (scrambled RNA) and OnRS/MGA (malachite green aptamer)) were readily obtained from 1 l bacterial culture. Deep sequencing analyses revealed that mature miR-124 and target GFP-siRNA were selectively released from chimeric RNAs in human cells. Consequently, OnRS/miR-124 was active in suppressing miR-124 target gene expression and controlling cellular processes, and OnRS/GFP-siRNA was effective in knocking down GFP mRNA levels and fluorescent intensity in ES-2/GFP cells and GFP-transgenic mice. Furthermore, the OnRS/MGA sensor offered a specific strong fluorescence upon binding MG, which was utilized as label-free substrate to accurately determine serum RNase activities in pancreatic cancer patients. These results demonstrate that OnRS-based bioengineering is a common, robust and versatile strategy to assemble various types of small RNAs for broad applications.

A semi-supervised learning approach for RNA secondary structure prediction

RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited.

RNA secondary structure prediction from multi-aligned sequences

It has been well accepted that the RNA secondary structures of most functional non-coding RNAs (ncRNAs) are closely related to their functions and are conserved during evolution. Hence, prediction of conserved secondary structures from evolutionarily related sequences is one important task in RNA bioinformatics; the methods are useful not only to further functional analyses of ncRNAs but also to improve the accuracy of secondary structure predictions and to find novel functional RNAs from the genome. In this review, I focus on common secondary structure prediction from a given aligned RNA sequence, in which one secondary structure whose length is equal to that of the input alignment is predicted. I systematically review and classify existing tools and algorithms for the problem, by utilizing the information employed in the tools and by adopting a unified viewpoint based on maximum expected gain (MEG) estimators. I believe that this classification will allow a deeper understanding of each tool and provide users with useful information for selecting tools for common secondary structure predictions.

Rapid production of novel pre-microRNA agent hsa-mir-27b in Escherichia coli using recombinant RNA technology for functional studies in mammalian cells

Noncoding microRNAs (miRNAs or miRs) have been revealed as critical epigenetic factors in the regulation of various cellular processes, including drug metabolism and disposition. However, research on miRNA functions is limited to the use of synthetic RNA and recombinant DNA agents. Herein, we show that novel pre-miRNA-27b (miR-27b) agents can be biosynthesized in Escherichia coli using recombinant RNA technology, and recombinant transfer RNA (tRNA)/mir-27b chimera was readily purified to a high degree of homogeneity (>95%) using anion-exchange fast protein liquid chromatography. The tRNA-fusion miR-27b was revealed to be processed to mature miRNA miR-27b in human carcinoma LS-180 cells in a dose- and time-dependent manner. Moreover, recombinant tRNA/miR-27b agents were biologically active in reducing the mRNA and protein expression levels of cytochrome P450 3A4 (CYP3A4), which consequently led to lower midazolam 1'-hydroxylase activity. These findings demonstrate that pre-miRNA agents can be produced by recombinant RNA technology for functional studies.

A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

Background

Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information.

Results

We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0.

Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure.

Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets.

Conclusions

Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php.

MicroRNA miR-92a-1 biogenesis and mRNA targeting is modulated by a tertiary contact within the miR-17~92 microRNA cluster

While functional mature microRNAs (miRNAs) are small ∼22 base oligonucleotides that target specific mRNAs, miRNAs are initially expressed as long transcripts (pri-miRNAs) that undergo sequential processing to yield the mature miRNAs. We have previously reported that the pri-miR-17∼92 cluster adopts a compact globular folded structure that internalizes a 3' core domain resulting in reduced miRNA maturation and subsequent mRNA targeting. Using a site-specific photo-cross-linker we have identified a tertiary contact within the 3' core domain of the pri-miRNA between a non-miRNA stem-loop and the pre-miR-19b hairpin. This tertiary contact is involved in the formation of the compact globular fold of the cluster while its disruption enhances miR-92a expression and mRNA targeting. We propose that this tertiary contact serves as a molecular scaffold to restrict expression of the proposed antiangiogenic miR-92a, allowing for the overall pro-angiogenic effect of miR-17∼92 expression.

Direct updating of an RNA base-pairing probability matrix with marginal probability constraints

A base-pairing probability matrix (BPPM) stores the probabilities for every possible base pair in an RNA sequence and has been used in many algorithms in RNA informatics (e.g., RNA secondary structure prediction and motif search). In this study, we propose a novel algorithm to perform iterative updates of a given BPPM, satisfying marginal probability constraints that are (approximately) given by recently developed biochemical experiments, such as SHAPE, PAR, and FragSeq. The method is easily implemented and is applicable to common models for RNA secondary structures, such as energy-based or machine-learning-based models. In this article, we focus mainly on the details of the algorithms, although preliminary computational experiments will also be presented.

CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks.

A classification of bioinformatics algorithms from the viewpoint of maximizing expected accuracy (MEA)

Many estimation problems in bioinformatics are formulated as point estimation problems in a high-dimensional discrete space. In general, it is difficult to design reliable estimators for this type of problem, because the number of possible solutions is immense, which leads to an extremely low probability for every solution-even for the one with the highest probability. Therefore, maximum score and maximum likelihood estimators do not work well in this situation although they are widely employed in a number of applications. Maximizing expected accuracy (MEA) estimation, in which accuracy measures of the target problem and the entire distribution of solutions are considered, is a more successful approach. In this review, we provide an extensive discussion of algorithms and software based on MEA. We describe how a number of algorithms used in previous studies can be classified from the viewpoint of MEA. We believe that this review will be useful not only for users wishing to utilize software to solve the estimation problems appearing in this article, but also for developers wishing to design algorithms on the basis of MEA.

pre-miRNA profiles obtained through application of locked nucleic acids and deep sequencing reveals complex 5'/3' arm variation including concomitant cleavage and polyuridylation patterns

Recent research hints at an underappreciated complexity in pre-miRNA processing and regulation. Global profiling of pre-miRNA and its potential to increase understanding of the pre-miRNA landscape is impeded by overlap with highly expressed classes of other non coding (nc) RNA. Here, we present a data set excluding these RNA before sequencing through locked nucleic acids (LNA), greatly increasing pre-miRNA sequence counts with no discernable effect on pre-miRNA or mature miRNA sequencing. Analysis of profiles generated in total, nuclear and cytoplasmic cell fractions reveals that pre-miRNAs are subject to a wide range of regulatory processes involving loci-specific 3′- and 5′-end variation entailing complex cleavage patterns with co-occurring polyuridylation. Additionally, examination of nuclear-enriched flanking sequences of pre-miRNA, particularly those derived from polycistronic miRNA transcripts, provides insight into miRNA and miRNA-offset (moRNA) production, specifically identifying novel classes of RNA potentially functioning as moRNA precursors. Our findings point to particularly intricate regulation of the let-7 family in many ways reminiscent of DICER1-independent, pre-mir-451-like processing, introduce novel and unify known forms of pre-miRNA regulation and processing, and shed new light on overlooked products of miRNA processing pathways.