KnotSeeker: heuristic pseudoknot detection in long RNA sequences

Pseudoknots in RNA Structure Prediction

RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases. © 2023 Wiley Periodicals LLC.

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model

Motivation

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method.

Results

The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

In Silico Prediction of RNA Secondary Structure

The secondary structure of an RNA molecule represents the base-pairing interactions within the molecule and fundamentally determines its overall structure. In this chapter, we overview the main approaches and existing tools for predicting RNA secondary structures, as well as methods for identifying noncoding RNAs from genomic sequences or RNA sequencing data. We then focus on the identification of a well-known class of small noncoding RNAs, namely microRNAs, which play very important roles in many biological processes through regulating post-transcriptionally the expression of genes and which dysregulation has been shown to be involved in several human diseases.

The complete genome sequence of a novel maize-associated totivirus

Deep sequencing of small RNA (sRNA) populations in maize plants from southwest China resulted in the identification of a previously unknown dsRNA virus with a sequence and genome organization resembling that of a totivirus. The complete viral genome is 3,956 nucleotides in length and contains two open reading frames (ORFs) with the potential to produce a ORF1-ORF2 fusion protein through a -1 ribosomal frameshift translation mechanism. ORF1 encodes the putative capsid protein (CP), whereas the predicted product of ORF2 contains motifs typical of an RNA-dependent RNA polymerase (RdRp). Phylogenetic analysis using the amino acid sequences of putative RdRp fusion proteins showed that the new virus was grouped in a clade together with the totiviruses, suggesting that it is a new member of the genus Totivirus of the family Totiviridae. The virus is tentatively named "maize-associated totivirus (MATV)". Our findings demonstrate that it is feasible to identify totiviruses by deep sequencing of small RNAs.

The putative Leishmania telomerase RNA (LeishTER) undergoes trans-splicing and contains a conserved template sequence

Telomerase RNAs (TERs) are highly divergent between species, varying in size and sequence composition. Here, we identify a candidate for the telomerase RNA component of Leishmania genus, which includes species that cause leishmaniasis, a neglected tropical disease. Merging a thorough computational screening combined with RNA-seq evidence, we mapped a non-coding RNA gene localized in a syntenic locus on chromosome 25 of five Leishmania species that shares partial synteny with both Trypanosoma brucei TER locus and a putative TER candidate-containing locus of Crithidia fasciculata. Using target-driven molecular biology approaches, we detected a ∼2,100 nt transcript (LeishTER) that contains a 5′ spliced leader (SL) cap, a putative 3′ polyA tail and a predicted C/D box snoRNA domain. LeishTER is expressed at similar levels in the logarithmic and stationary growth phases of promastigote forms. A 5′SL capped LeishTER co-immunoprecipitated and co-localized with the telomerase protein component (TERT) in a cell cycle-dependent manner. Prediction of its secondary structure strongly suggests the existence of a bona fide single-stranded template sequence and a conserved C[U/C]GUCA motif-containing helix II, representing the template boundary element. This study paves the way for further investigations on the biogenesis of parasite TERT ribonucleoproteins (RNPs) and its role in parasite telomere biology.

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.

Longitudinal analysis of intra-host simian immunodeficiency virus recombination in varied tissues of the rhesus macaque model for neuroAIDS

Human immunodeficiency virus intra-host recombination has never been studied in vivo both during early infection and throughout disease progression. The CD8-depleted rhesus macaque model of neuroAIDS was used to investigate the impact of recombination from early infection up to the onset of neuropathology in animals inoculated with a simian immunodeficiency virus (SIV) swarm. Several lymphoid and non-lymphoid tissues were collected longitudinally at 21 days post-infection (p.i.), 61 days p.i. and necropsy (75-118 days p.i.) from four macaques that developed SIV-encephalitis or meningitis, as well as from two animals euthanized at 21 days p.i. The number of recombinant sequences and breakpoints in different tissues and over time from each primate were compared. Breakpoint locations were mapped onto predicted RNA and protein secondary structures. Recombinants were found at each time point and in each primate as early as 21 days p.i. No association was found between recombination rates and specific tissue of origin. Several identical breakpoints were identified in sequences derived from different tissues in the same primate and among different primates. Breakpoints predominantly mapped to unpaired nucleotides or pseudoknots in RNA secondary structures, and proximal to glycosylation sites and cysteine residues in protein sequences, suggesting selective advantage in the emergence of specific recombinant sequences. Results indicate that recombinant sequences can become fixed very early after infection with a heterogeneous viral swarm. Features of RNA and protein secondary structure appear to play a role in driving the production of recombinants and their selection in the rapid disease model of neuroAIDS.

Computational analysis of noncoding RNAs

Noncoding RNAs have emerged as important key players in the cell. Understanding their surprisingly diverse range of functions is challenging for experimental and computational biology. Here, we review computational methods to analyze noncoding RNAs. The topics covered include basic and advanced techniques to predict RNA structures, annotation of noncoding RNAs in genomic data, mining RNA-seq data for novel transcripts and prediction of transcript structures, computational aspects of microRNAs, and database resources.

Evolutionary genomics of mycovirus-related dsRNA viruses reveals cross-family horizontal gene transfer and evolution of diverse viral lineages

Background

Double-stranded (ds) RNA fungal viruses are typically isometric single-shelled particles that are classified into three families, Totiviridae, Partitiviridae and Chrysoviridae, the members of which possess monopartite, bipartite and quadripartite genomes, respectively. Recent findings revealed that mycovirus-related dsRNA viruses are more diverse than previously recognized. Although an increasing number of viral complete genomic sequences have become available, the evolution of these diverse dsRNA viruses remains to be clarified. This is particularly so since there is little evidence for horizontal gene transfer (HGT) among dsRNA viruses.

Results

In this study, we report the molecular properties of two novel dsRNA mycoviruses that were isolated from a field strain of Sclerotinia sclerotiorum, Sunf-M: one is a large monopartite virus representing a distinct evolutionary lineage of dsRNA viruses; the other is a new member of the family Partitiviridae. Comprehensive phylogenetic analysis and genome comparison revealed that there are at least ten monopartite, three bipartite, one tripartite and three quadripartite lineages in the known dsRNA mycoviruses and that the multipartite lineages have possibly evolved from different monopartite dsRNA viruses. Moreover, we found that homologs of the S7 Domain, characteristic of members of the genus phytoreovirus in family Reoviridae are widely distributed in diverse dsRNA viral lineages, including chrysoviruses, endornaviruses and some unclassified dsRNA mycoviruses. We further provided evidence that multiple HGT events may have occurred among these dsRNA viruses from different families.

Conclusions

Our study provides an insight into the phylogeny and evolution of mycovirus-related dsRNA viruses and reveals that the occurrence of HGT between different virus species and the development of multipartite genomes during evolution are important macroevolutionary mechanisms in dsRNA viruses.

Identifying complete RNA structural ensembles including pseudoknots

The close relationship between RNA structure and function underlines the significance of accurately predicting RNA structures from sequence information. Structural topologies such as pseudoknots are of particular interest due to their ubiquity and direct involvement in RNA function, but identifying pseudoknots is a computationally challenging problem and existing heuristic approaches usually perform poorly for RNA sequences of even a few hundred bases. We survey the performance of pseudoknot prediction methods on a data set of full-length RNA sequences representing varied sequence lengths, and biological RNA classes such as RNase P RNA, Group I Intron, tmRNA and tRNA. Pseudoknot prediction methods are compared with minimum free energy and suboptimal secondary structure prediction methods in terms of correct base-pairs, stems and pseudoknots and we find that the ensemble of suboptimal structure predictions succeeds in identifying correct structural elements in RNA that are usually missed in MFE and pseudoknot predictions. We propose a strategy to identify a comprehensive set of non-redundant stems in the suboptimal structure space of a RNA molecule by applying heuristics that reduce the structural redundancy of the predicted suboptimal structures by merging slightly varying stems that are predicted to form in local sequence regions. This reduced-redundancy set of structural elements consistently outperforms more specialized approaches.in data sets. Thus, the suboptimal folding space can be used to represent the structural diversity of an RNA molecule more comprehensively than optimal structure prediction approaches alone.

Annexin A2 binds RNA and reduces the frameshifting efficiency of infectious bronchitis virus

Annexin A2 (ANXA2) is a protein implicated in diverse cellular functions, including exocytosis, DNA synthesis and cell proliferation. It was recently proposed to be involved in RNA metabolism because it was shown to associate with some cellular mRNA. Here, we identified ANXA2 as a RNA binding protein (RBP) that binds IBV (Infectious Bronchitis Virus) pseudoknot RNA. We first confirmed the binding of ANXA2 to IBV pseudoknot RNA by ultraviolet crosslinking and showed its binding to RNA pseudoknot with ANXA2 protein in vitro and in the cells. Since the RNA pseudoknot located in the frameshifting region of IBV was used as bait for cellular RBPs, we tested whether ANXA2 could regulate the frameshfting of IBV pseudoknot RNA by dual luciferase assay. Overexpression of ANXA2 significantly reduced the frameshifting efficiency from IBV pseudoknot RNA and knockdown of the protein strikingly increased the frameshifting efficiency. The results suggest that ANXA2 is a cellular RBP that can modulate the frameshifting efficiency of viral RNA, enabling it to act as an anti-viral cellular protein, and hinting at roles in RNA metabolism for other cellular mRNAs.

Blueberry latent virus: an amalgam of the Partitiviridae and Totiviridae

A new, symptomless virus was identified in blueberry. The dsRNA genome of the virus, provisionally named Blueberry latent virus (BBLV), codes for two putative proteins, one without any similarities to virus proteins and an RNA-dependent RNA polymerase. More than 35 isolates of the virus from different cultivars and geographic regions were partially or completely sequenced. BBLV, found in more than 50% of the material tested, has high degree of homogeneity as isolates show more than 99% nucleotide identity between them. Phylogenetic analysis clearly shows a close relationship between BBLV and members of the Partitiviridae, although its genome organization is related more closely to members of the Totiviridae. Transmission studies from three separate crosses showed that the virus is transmitted very efficiently by seed. These properties suggest that BBLV belongs to a new family of plant viruses with unique genome organization for a plant virus but signature properties of cryptic viruses including symptomless infection and very efficient vertical transmission.

A novel monopartite dsRNA virus from rhododendron

A dsRNA molecule of 3.4 kbp was extracted from two great rhododendron samples from Great Smoky Mountains National Park. Sequencing of this molecule suggests that it represents the genome of an undescribed virus, for which the provisional name rhododendron virus A (RhVA) is proposed. In phylogenetic analyses, this virus clustered together with southern tomato virus and related viruses, forming a coherent and distinct clade among dsRNA viruses. RhVA likely belongs to a yet-to-be-established taxon of viruses with a non-segmented dsRNA genome.

A comparative taxonomy of parallel algorithms for RNA secondary structure prediction

RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods.

An electrochemical sensor for single nucleotide polymorphism detection in serum based on a triple-stem DNA probe

We report here an electrochemical approach that offers, for the first time, single-step, room-temperature single nucleotide polymorphism (SNP) detection directly in complex samples (such as blood serum) without the need for target modification, postwashing, or the addition of exogenous reagents. This sensor, which is sensitive, stable, and reusable, is comprised of a single, self-complementary, methylene blue-labeled DNA probe possessing a triple-stem structure. This probe takes advantage of the large thermodynamic changes in enthalpy and entropy that result from major conformational rearrangements that occur upon binding a perfectly matched target, resulting in a large-scale change in the faradaic current. As a result, the discrimination capabilities of this sensor greatly exceed those of earlier single- and double-stem electrochemical sensors and support rapid (minutes), single-step, reagentless, room-temperature detection of single nucleotide substitutions. To elucidate the theoretical basis of the sensor's selectivity, we present a comparative thermodynamic analysis among single-, double-, and triple-stem probes.

DotKnot: pseudoknot prediction using the probability dot plot under a refined energy model

RNA pseudoknots are functional structure elements with key roles in viral and cellular processes. Prediction of a pseudoknotted minimum free energy structure is an NP-complete problem. Practical algorithms for RNA structure prediction including restricted classes of pseudoknots suffer from high runtime and poor accuracy for longer sequences. A heuristic approach is to search for promising pseudoknot candidates in a sequence and verify those. Afterwards, the detected pseudoknots can be further analysed using bioinformatics or laboratory techniques. We present a novel pseudoknot detection method called DotKnot that extracts stem regions from the secondary structure probability dot plot and assembles pseudoknot candidates in a constructive fashion. We evaluate pseudoknot free energies using novel parameters, which have recently become available. We show that the conventional probability dot plot makes a wide class of pseudoknots including those with bulged stems manageable in an explicit fashion. The energy parameters now become the limiting factor in pseudoknot prediction. DotKnot is an efficient method for long sequences, which finds pseudoknots with higher accuracy compared to other known prediction algorithms. DotKnot is accessible as a web server at http://dotknot.csse.uwa.edu.au.

Fluorescence detection of single-nucleotide polymorphisms with a single, self-complementary, triple-stem DNA probe

Singled out for its singularity: In a single-step, single-component, fluorescence-based method for the detection of single-nucleotide polymorphisms at room temperature, the sensor is comprised of a single, self-complementary DNA strand that forms a triple-stem structure. The large conformational change that occurs upon binding to perfectly matched (PM) targets results in a significant increase in fluorescence (see picture; F = fluorophore, Q = quencher).

Predicting structures and stabilities for H-type pseudoknots with interhelix loops

RNA pseudoknots play a critical role in RNA-related biology from the assembly of ribosome to the regulation of viral gene expression. A predictive model for pseudoknot structure and stability is essential for understanding and designing RNA structure and function. A previous statistical mechanical theory allows us to treat canonical H-type RNA pseudoknots that contain no intervening loop between the helices (see S. Cao and S.J. Chen [2006] in Nucleic Acids Research, Vol. 34; pp. 2634-2652). Biologically significant RNA pseudoknots often contain interhelix loops. Predicting the structure and stability for such more-general pseudoknots remains an unsolved problem. In the present study, we develop a predictive model for pseudoknots with interhelix loops. The model gives conformational entropy, stability, and the free-energy landscape from RNA sequences. The main features of this new model are the computation of the conformational entropy and folding free-energy base on the complete conformational ensemble and rigorous treatment for the excluded volume effects. Extensive tests for the structural predictions show overall good accuracy with average sensitivity and specificity equal to 0.91 and 0.91, respectively. The theory developed here may be a solid starting point for first-principles modeling of more complex, larger RNAs.

Southern tomato virus: The link between the families Totiviridae and Partitiviridae

A dsRNA virus with a genome of 3.5 kb was isolated from field and greenhouse-grown tomato plants of different cultivars and geographic locations in North America. Cloning and sequencing of the viral genome showed the presence of two partially overlapping open reading frames (ORFs), and a genomic organization resembling members of the family Totiviridae that comprises fungal and protozoan viruses, but not plant viruses. The 5'-proximal ORF codes for a 377 amino acid-long protein of unknown function, whereas the product of ORF2 contains typical motifs of an RNA-dependant RNA-polymerase and is likely expressed by a +1 ribosomal frame shift. Despite the similarity in the genome organization with members of the family Totiviridae, this virus shared very limited sequence homology with known totiviruses or with other viruses. Repeated attempts to detect the presence of an endophytic fungus as the possible host of the virus failed, supporting its phytoviral nature. The virus was efficiently transmitted by seed but not mechanically and/or by grafting. Phylogenetic analyses revealed that this virus, for which the name Southern tomato virus (STV) is proposed, belongs to a partitivirus-like lineage and represents a species of a new taxon of plant viruses.