G4RNA: an RNA G-quadruplex database

Clusters of mammalian conserved RNA structures in UTRs associate with RBP binding sites

RNA secondary structures play essential roles in the formation of the tertiary structure and function of a transcript. Recent genome-wide studies highlight significant potential for RNA structures in the mammalian genome. However, a major challenge is assigning functional roles to these structured RNAs. In this study, we conduct a guilt-by-association analysis of clusters of computationally predicted conserved RNA structure (CRSs) in human untranslated regions (UTRs) to associate them with gene functions. We filtered a broad pool of ∼500 000 human CRSs for UTR overlap, resulting in 4734 and 24 754 CRSs from the 5' and 3' UTR of protein-coding genes, respectively. We separately clustered these CRSs for both sets using RNAscClust, obtaining 793 and 2403 clusters, each containing an average of five CRSs per cluster. We identified overrepresented binding sites for 60 and 43 RNA-binding proteins co-localizing with the clustered CRSs. Furthermore, 104 and 441 clusters from the 5' and 3' UTRs, respectively, showed enrichment for various Gene Ontologies, including biological processes such as 'signal transduction', 'nervous system development', molecular functions like 'transferase activity' and the cellular components such as 'synapse' among others. Our study shows that significant functional insights can be gained by clustering RNA structures based on their structural characteristics.

G4Bank: A database of experimentally identified DNA G-quadruplex sequences

G-quadruplex (G4), a non-canonical nucleic acid structure, has been suggested to play a key role in important cellular processes including transcription, replication and cancer development. Recently, high-throughput sequencing approaches for G4 detection have provided a large amount of experimentally identified G4 data that reveal genome-wide G4 landscapes and enable the development of new methods for predicting potential G4s from sequences. Although several existing databases provide G4 experimental data and relevant biological information from different perspectives, there is no dedicated database to collect and analyze DNA G4 experimental data genome-widely. Here, we constructed G4Bank, a database of experimentally identified DNA G-quadruplex sequences. A total of 6,915,983 DNA G4s were collected from 13 organisms, and state-of-the-art prediction methods were performed to filter and analyze the G4 data. Therefore, G4Bank will facilitate users to access comprehensive G4 experimental data and enable sequence feature analysis of G4 for further investigation. The database of the experimentally identified DNA G-quadruplex sequences can be accessed at http://tubic.tju.edu.cn/g4bank/ .

An overview on nucleic-acid G-quadruplex prediction: from rule-based methods to deep neural networks

Nucleic-acid G-quadruplexes (G4s) play vital roles in many cellular processes. Due to their importance, researchers have developed experimental assays to measure nucleic-acid G4s in high throughput. The generated high-throughput datasets gave rise to unique opportunities to develop machine-learning-based methods, and in particular deep neural networks, to predict G4s in any given nucleic-acid sequence and any species. In this paper, we review the success stories of deep-neural-network applications for G4 prediction. We first cover the experimental technologies that generated the most comprehensive nucleic-acid G4 high-throughput datasets in recent years. We then review classic rule-based methods for G4 prediction. We proceed by reviewing the major machine-learning and deep-neural-network applications to nucleic-acid G4 datasets and report a novel comparison between them. Next, we present the interpretability techniques used on the trained neural networks to learn key molecular principles underlying nucleic-acid G4 folding. As a new result, we calculate the overlap between measured DNA and RNA G4s and compare the performance of DNA- and RNA-G4 predictors on RNA- and DNA-G4 datasets, respectively, to demonstrate the potential of transfer learning from DNA G4s to RNA G4s. Last, we conclude with open questions in the field of nucleic-acid G4 prediction and computational modeling.

Structural and Functional Classification of G-Quadruplex Families within the Human Genome

G-quadruplexes (G4s) are short secondary DNA structures located throughout genomic DNA and transcribed RNA. Although G4 structures have been shown to form in vivo, no current search tools that examine these structures based on previously identified G-quadruplexes and filter them based on similar sequence, structure, and thermodynamic properties are known to exist. We present a framework for clustering G-quadruplex sequences into families using the CD-HIT, MeShClust, and DNACLUST methods along with a combination of Starcode and BLAST. Utilizing this framework to filter and annotate clusters, 95 families of G-quadruplex sequences were identified within the human genome. Profiles for each family were created using hidden Markov models to allow for the identification of additional family members and generate homology probability scores. The thermodynamic folding energy properties, functional annotation of genes associated with the sequences, scores from different prediction algorithms, and transcription factor binding motifs within a family were used to annotate and compare the diversity within and across clusters. The resulting set of G-quadruplex families can be used to further understand how different regions of the genome are regulated by factors targeting specific structures common to members of a specific cluster.

rG4detector, a novel RNA G-quadruplex predictor, uncovers their impact on stress granule formation

RNA G-quadruplexes (rG4s) are RNA secondary structures, which are formed by guanine-rich sequences and have important cellular functions. Existing computational tools for rG4 prediction rely on specific sequence features and/or were trained on small datasets, without considering rG4 stability information, and are therefore sub-optimal. Here, we developed rG4detector, a convolutional neural network to identify potential rG4s in transcriptomics data. rG4detector outperforms existing methods in both predicting rG4 stability and in detecting rG4-forming sequences. To demonstrate the biological-relevance of rG4detector, we employed it to study RNAs that are bound by the RNA-binding protein G3BP1. G3BP1 is central to the induction of stress granules (SGs), which are cytoplasmic biomolecular condensates that form in response to a variety of cellular stresses. Unexpectedly, rG4detector revealed a dynamic enrichment of rG4s bound by G3BP1 in response to cellular stress. In addition, we experimentally characterized G3BP1 cross-talk with rG4s, demonstrating that G3BP1 is a bona fide rG4-binding protein and that endogenous rG4s are enriched within SGs. Furthermore, we found that reduced rG4 availability impairs SG formation. Hence, we conclude that rG4s play a direct role in SG biology via their interactions with RNA-binding proteins and that rG4detector is a novel useful tool for rG4 transcriptomics data analyses.

G4Atlas: a comprehensive transcriptome-wide G-quadruplex database

RNA G-quadruplex (rG4) is a vital RNA tertiary structure motif that involves the base pairs on both Hoogsteen and Watson-Crick faces of guanines. rG4 is of great importance in the post-transcriptional regulation of gene expression. Experimental technologies have advanced to identify in vitro and in vivo rG4s across diverse transcriptomes. Building on these recent advances, here we present G4Atlas, the first transcriptome-wide G-quadruplex database, in which we have collated, classified, and visualized transcriptome rG4 experimental data, generated from rG4-seq, chemical profiling and ligand-binding methods. Our comprehensive database includes transcriptome-wide rG4s generated from 82 experimental treatments and 238 samples across ten species. In addition, we have also included RNA secondary structure prediction information across both experimentally identified and unidentified rG4s to enable users to display any potential competitive folding between rG4 and RNA secondary structures. As such, G4Atlas will enable users to explore the general functions of rG4s in diverse biological processes. In addition, G4Atlas lays the foundation for further data-driven deep learning algorithms to examine rG4 structural features.

QUADRatlas: the RNA G-quadruplex and RG4-binding proteins database

RNA G-quadruplexes (RG4s) are non-canonical, disease-associated post-transcriptional regulators of gene expression whose functions are driven by RNA-binding proteins (RBPs). Being able to explore transcriptome-wide RG4 formation and interaction with RBPs is thus paramount to understanding how they are regulated and exploiting them as potential therapeutic targets. Towards this goal, we present QUADRatlas (https://rg4db.cibio.unitn.it), a database of experimentally-derived and computationally predicted RG4s in the human transcriptome, enriched with biological function and disease associations. As RBPs are key to their function, we mined known interactions of RG4s with such proteins, complemented with an extensive RBP binding sites dataset. Users can thus intersect RG4s with their potential regulators and effectors, enabling the formulation of novel hypotheses on RG4 regulation, function and pathogenicity. To support this capability, we provide analysis tools for predicting whether an RBP can bind RG4s, RG4 enrichment in a gene set, and de novo RG4 prediction. Genome-browser and table views allow exploring, filtering, and downloading the data quickly for individual genes and in batch. QUADRatlas is a significant step forward in our ability to understand the biology of RG4s, offering unmatched data content and enabling the integrated analysis of RG4s and their interactions with RBPs.

GAIA: G-quadruplexes in alive creature database

G-quadruplexes (G4) are 3D structures that are found in both DNA and RNA. Interest in this structure has grown over the past few years due to both its implication in diverse biological mechanisms and its potential use as a therapeutic target, to name two examples. G4s in humans have been widely studied; however, the level of their study in other species remains relatively minimal. That said, progress in this field has resulted in the prediction of G4s structures in various species, ranging from bacteria to eukaryotes. These predictions were analysed in a previous study which revealed that G4s are present in all living kingdoms. To date, eleven different databases have grouped the various G4s depending on either their structures, on the proteins that might bind them, or on their location in the various genomes. However, none of these databases contains information on their location in the transcriptome of many of the implicated species. The GAIA database was designed so as to make this data available online in a user-friendly manner. Through its web interface, users can query GAIA to filter G4s, which, we hope, will help the research in this field. GAIA is available at: https://gaia.cobius.usherbrooke.ca.

Deep Learning in RNA Structure Studies

Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.

A Machine Learning Perspective on DNA and RNA G-quadruplexes

Abstract:

G-quadruplexes (G4s) are particular structures found in guanine-rich DNA and RNA sequences that exhibit a wide diversity of three-dimensional conformations and exert key functions in the control of gene expression. G4s are able to interact with numerous small molecules and endogenous proteins, and their dysregulation can lead to a variety of disorders and diseases. Characterization and prediction of G4-forming sequences could elucidate their mechanism of action and could thus represent an important step in the discovery of potential therapeutic drugs. In this perspective, we propose an overview of G4s, discussing the state of the art of methodologies and tools developed to characterize and predict the presence of these structures in genomic sequences. In particular, we report on machine learning (ML) approaches and artificial neural networks (ANNs) that could open new avenues for the accurate analysis of quadruplexes, given their potential to derive informative features by learning from large, high-density datasets.

ONQUADRO: a database of experimentally determined quadruplex structures

ONQUADRO is an advanced database system that supports the study of the structures of canonical and non-canonical quadruplexes. It combines a relational database that collects comprehensive information on tetrads, quadruplexes, and G4-helices; programs to compute structure parameters and visualise the data; scripts for statistical analysis; automatic updates and newsletter modules; and a web application that provides a user interface. The database is a self-updating resource, with new information arriving once a week. The preliminary data are downloaded from the Protein Data Bank, processed, annotated, and completed. As of August 2021, ONQUADRO contains 1,661 tetrads, 518 quadruplexes, and 30 G4-helices found in 467 experimentally determined 3D structures of nucleic acids. Users can view and download their description: sequence, secondary structure (dot-bracket, classical diagram, arc diagram), tertiary structure (ball-and-stick, surface or vdw-ball model, layer diagram), planarity, twist, rise, chi angle (value and type), loop characteristics, strand directionality, metal ions, ONZ, and Webba da Silva classification (the latter by loop topology and tetrad combination), origin structure ID, assembly ID, experimental method, and molecule type. The database is freely available at https://onquadro.cs.put.poznan.pl/. It can be used on both desktop computers and mobile devices.

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.

ProTG4: A Web Server to Approximate the Sequence of a Generic Protein From an in Silico Library of Translatable G-Quadruplex (TG4)-Mapped Peptides

An RNA G-quadruplex in the protein coding segment of mRNA is translatable (TG4) and may potentially impact protein translation. This can be consequent to staggered ribosomal synthesis and/or result in an increased frequency of missense translational events. A mathematical model of the peptides that encompass the substituted amino acids, ie, the TG4-mapped peptidome, has been previously studied. However, the significance and relevance to disease biology of this model remains to be established. ProTG4 computes a confidence-of-sequence-identity (γ)-score, which is the average weighted length of every matched TG4-mapped peptide in a generic protein sequence. The weighted length is the product of the length of the peptide and the probability of its non-random occurrence in a library of randomly generated sequences of equivalent lengths. This is then averaged over the entire length of the protein sequence. ProTG4 is simple to operate, has clear instructions, and is accompanied by a set of ready-to-use examples. The rationale of the study, algorithms deployed, and the computational pipeline deployed are also part of the web page. Analyses by ProTG4 of taxonomically diverse protein sequences suggest that there is significant homology to TG4-mapped peptides. These findings, especially in potentially infectious and infesting agents, offer plausible explanations into the aetiology and pathogenesis of certain proteopathies. ProTG4 can also provide a quantitative measure to identify and annotate the canonical form of a generic protein sequence from its known isoforms. The article presents several case studies and discusses the relevance of ProTG4-assisted peptide analysis in gaining insights into various mechanisms of disease biology (mistranslation, alternate splicing, amino acid substitutions).

Action and function of helicases on RNA G-quadruplexes

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Methodological progresses and piling evidence prove the rG4 biology in vivo.

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rG4s step in virtually every aspect of RNA biology.

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Helicases unwinding of rG4s is a fine regulatory layer to the downstream processes and general cell homeostasis.

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The current knowledge is however limited to a few cell lines.

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The regulation of helicases themselves is delineating as a important question.

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Non-helicase rG4-processing proteins likely play a role.

The nucleic acid structure called G-quadruplex (G4) is currently discussed to function in nucleic acid-based mechanisms that influence several cellular processes. They can modulate the cellular machinery either positively or negatively, both at the DNA and RNA level. The majority of what we know about G4 biology comes from DNA G4 (dG4) research. RNA G4s (rG4), on the other hand, are gaining interest as researchers become more aware of their role in several aspects of cellular homeostasis. In either case, the correct regulation of G4 structures within cells is essential and demands specialized proteins able to resolve them. Small changes in the formation and unfolding of G4 structures can have severe consequences for the cells that could even stimulate genome instability, apoptosis or proliferation. Helicases are the most relevant negative G4 regulators, which prevent and unfold G4 formation within cells during different pathways. Yet, and despite their importance only a handful of rG4 unwinding helicases have been identified and characterized thus far. This review addresses the current knowledge on rG4s-processing helicases with a focus on methodological approaches. An example of a non-helicase rG4s-unwinding protein is also briefly described.

RNA G-quadruplexes (rG4s): genomics and biological functions

G-quadruplexes (G4s) are non-classical DNA or RNA secondary structures that have been first observed decades ago. Over the years, these four-stranded structural motifs have been demonstrated to have significant regulatory roles in diverse biological processes, but challenges remain in detecting them globally and reliably. Compared to DNA G4s (dG4s), the study of RNA G4s (rG4s) has received less attention until recently. In this review, we will summarize the innovative high-throughput methods recently developed to detect rG4s on a transcriptome-wide scale, highlight the many novel and important functions of rG4 being discovered in vivo across the tree of life, and discuss the key biological questions to be addressed in the near future.

Topology-based classification of tetrads and quadruplex structures

Motivation

Quadruplexes attract the attention of researchers from many fields of bio-science. Due to a specific structure, these tertiary motifs are involved in various biological processes. They are also promising therapeutic targets in many strategies of drug development, including anticancer and neurological disease treatment. The uniqueness and diversity of their forms cause that quadruplexes show great potential in novel biological applications. The existing approaches for quadruplex analysis are based on sequence or 3D structure features and address canonical motifs only.

Results

In our study, we analyzed tetrads and quadruplexes contained in nucleic acid molecules deposited in Protein Data Bank. Focusing on their secondary structure topology, we adjusted its graphical diagram and proposed new dot-bracket and arc representations. We defined the novel classification of these motifs. It can handle both canonical and non-canonical cases. Based on this new taxonomy, we implemented a method that automatically recognizes the types of tetrads and quadruplexes occurring as unimolecular structures. Finally, we conducted a statistical analysis of these motifs found in experimentally determined nucleic acid structures in relation to the new classification.

Availability and implementation

https://github.com/tzok/eltetrado/

Supplementary information

Supplementary data are available at Bioinformatics online.

How bioinformatics resources work with G4 RNAs

Quadruplexes (G4s) are of interest, which increases with the number of identified G4 structures and knowledge about their biomedical potential. These unique motifs form in many organisms, including humans, where their appearance correlates with various diseases. Scientists store and analyze quadruplexes using recently developed bioinformatic tools—many of them focused on DNA structures. With an expanding collection of G4 RNAs, we check how existing tools deal with them. We review all available bioinformatics resources dedicated to quadruplexes and examine their usefulness in G4 RNA analysis. We distinguish the following subsets of resources: databases, tools to predict putative quadruplex sequences, tools to predict secondary structure with quadruplexes and tools to analyze and visualize quadruplex structures. We share the results obtained from processing specially created RNA datasets with these tools.

Contact: mszachniuk@cs.put.poznan.pl

Supplementary information: Supplementary data are available at Briefings in Bioinformatics online.

Where are G-quadruplexes located in the human transcriptome?

It has been demonstrated that RNA G-quadruplexes (G4) are structural motifs present in transcriptomes and play important regulatory roles in several post-transcriptional mechanisms. However, the full picture of RNA G4 locations and the extent of their implication remain elusive. Solely computational prediction analysis of the whole transcriptome may reveal all potential G4, since experimental identifications are always limited to specific conditions or specific cell lines. The present study reports the first in-depth computational prediction of potential G4 region across the complete human transcriptome. Although using a relatively stringent approach based on three prediction scores that accounts for the composition of G4 sequences, the composition of their neighboring sequences, and the various forms of G4, over 1.1 million of potential G4 (pG4) were predicted. The abundance of G4 was computationally confirmed in both 5' and 3'UTR as well as splicing junction of mRNA, appreciate for the first time in the long ncRNA, while almost absent of most of the small ncRNA families. The present results constitute an important step toward a full understanding of the roles of G4 in post-transcriptional mechanisms.

A guide to computational methods for G-quadruplex prediction

Guanine-rich nucleic acids can fold into the non-B DNA or RNA structures called G-quadruplexes (G4). Recent methodological developments have allowed the characterization of specific G-quadruplex structures in vitro as well as in vivo, and at a much higher throughput, in silico, which has greatly expanded our understanding of G4-associated functions. Typically, the consensus motif G3+N1-7G3+N1-7G3+N1-7G3+ has been used to identify potential G-quadruplexes from primary sequence. Since, various algorithms have been developed to predict the potential formation of quadruplexes directly from DNA or RNA sequences and the number of studies reporting genome-wide G4 exploration across species has rapidly increased. More recently, new methodologies have also appeared, proposing other estimates which consider non-canonical sequences and/or structure propensity and stability. The present review aims at providing an updated overview of the current open-source G-quadruplex prediction algorithms and straightforward examples of their implementation.

Structural analysis and cellular visualization of APP RNA G-quadruplex

RNA G-quadruplexes (rG4s) are emerging structural motifs that are of pivotal importance in chemistry and biology; however, the current structural information of rG4s is limited, with their folding status and functions in cells remaining elusive. Here, we develop and employ a multi-disciplinary approach to characterize the structure, formation and function of an individual rG4 of interest in vitro and in cells. We apply this strategy to a biologically important rG4 in amyloid precursor protein (APP) transcript and reveal distinct structural features of APP rG4. Notably, we visualize the formation of APP rG4 in cells using an APP-specific G-quadruplex-triggered fluorogenic hybridization (GTFH) probe and report that the regulatory role of APP rG4 in translation is dependent on rG4 thermostability, providing evidence to the existence and significance of the stable rG4 structure in gene regulation.

[The potential of G-quadruplexes as a therapeutic target for neurological diseases]

The most common form of DNA is a right-handed helix, the B-form DNA. DNA can also adopt a variety of alternative conformations, termed non-B-form DNA secondary structures, including the G-quadruplex (G4). Furthermore, non-canonical RNA G4 secondary structures are also observed. Recent bioinformatics analysis revealed genomic positions of G4. In addition, G4 formation may be associated with various biological functions, including DNA replication, transcription, epigenetic modification, and RNA metabolism. In this review, we focus on G4 structures in neuronal functions, which may have important roles reveal mechanisms underlying neurological disorders. In addition, we discuss the potential of G4s as a therapeutic target for neurological diseases.

The diverse structural landscape of quadruplexes

G-quadruplexes are secondary structures formed in G-rich sequences in DNA and RNA. Considerable research over the past three decades has led to in-depth insight into these unusual structures in DNA. Since the more recent exploration into RNA G-quadruplexes, such structures have demonstrated their in cellulo existence, function and roles in pathology. In comparison to Watson-Crick-based secondary structures, most G-quadruplexes display highly redundant structural characteristics. However, numerous reports of G-quadruplex motifs/structures with unique features (e.g. bulges, long loops, vacancy) have recently surfaced, expanding the repertoire of G-quadruplex scaffolds. This review addresses G-quadruplex formation and structure, including recent reports of non-canonical G-quadruplex structures. Improved methods of detection will likely further expand this collection of novel structures and ultimately change the face of quadruplex-RNA targeting as a therapeutic strategy.

Perspectives for Applying G-Quadruplex Structures in Neurobiology and Neuropharmacology

The most common form of DNA is a right-handed helix or the B-form DNA. DNA can also adopt a variety of alternative conformations, non-B-form DNA secondary structures, including the DNA G-quadruplex (DNA-G4). Furthermore, besides stem-loops that yield A-form double-stranded RNA, non-canonical RNA G-quadruplex (RNA-G4) secondary structures are also observed. Recent bioinformatics analysis of the whole-genome and transcriptome obtained using G-quadruplex–specific antibodies and ligands, revealed genomic positions of G-quadruplexes. In addition, accumulating evidence pointed to the existence of these structures under physiologically- and pathologically-relevant conditions, with functional roles in vivo. In this review, we focused on DNA-G4 and RNA-G4, which may have important roles in neuronal function, and reveal mechanisms underlying neurological disorders related to synaptic dysfunction. In addition, we mention the potential of G-quadruplexes as therapeutic targets for neurological diseases.

Plant-GQ: An Integrative Database of G-Quadruplex in Plant

G-quadruplex (G-Q) is advanced DNA or RNA secondary structures frequently found in plant and involved in important biological processes such as transcription, translation, and telomere maintenance. Although some databases and tools were developed for predicting and studying G-Q, none of them was for plant. With the development of next-generation sequencing technology, a large number of plant genomes have been assembled and annotated to provide opportunities for mining G-Q. Plant G-quadruplex database (Plant-GQ) was constructed for predicting G-Q in 195 plants. It has a total of 626,341,645 predicted G-Qs. The database contains four major parts: Search, Tools, JBrowse, and Download. Not only G-Q information but also online forecasting tool can be retrieved and obtained from Plant-GQ. It can also browse and analyze G-Q information by JBrowse in a graph visualization interface. Considering the key role of G-Q in plant, this database will play an important status in the study of the structure, function, and biological relevance of G-Q in plant.

Stable G-quadruplex enabling sequences are selected against by the context-dependent codon bias

The distributions of secondary structural elements appear to differ between coding regions (CDS) of mRNAs compared to the untranslated regions (UTRs), presumably as a mechanism to fine-tune gene expression, including efficiency of translation. However, a systematic and comprehensive analysis of secondary structure avoidance because of potential bias in codon usage is difficult as some of the common secondary structures, such as, hairpins can be formed by numerous sequence combinations. Using G-quadruplex (GQ) as the model secondary structure we studied the impact of codon bias on GQs within the CDS. Because GQs can be predicted using specific consensus sequence motifs, they provide an excellent platform for investigation of the selectivity of such putative structures at the codon level. Using a bioinformatics approach, we calculated the frequencies of putative GQs within the CDS of a variety of species. Our results suggest that the most stable GQs appear to be significantly underrepresented within the CDS, through the use of specific synonymous codon combinations. Furthermore, we identified many peptide sequence motifs in which silent mutations can potentially alter translation via stable GQ formation. This work not only provides a comprehensive analysis on how stable secondary structures appear to be avoided within the CDS of mRNA, but also broadens the current understanding of synonymous codon usage as they relate to the structure-function relationship of RNA.

Non-duplex G-Quadruplex Structures Emerge as Mediators of Epigenetic Modifications

The role of non-duplex DNA, the guanine-quadruplex structure in particular, is becoming widely appreciated. Increasing evidence in the last decade implicates quadruplexes in important processes such as transcription and replication. Interestingly, more recent work suggests roles for quadruplexes, in association with quadruplex-interacting proteins, in epigenetics through both DNA and histone modifications. Here, we review the effect of the quadruplex structure on post-replication epigenetic memory and quadruplex-induced promoter DNA/histone modifications. Furthermore, we highlight the epigenetic state of the telomerase promoter where quadruplexes could play a key regulatory role. Finally, we discuss the possibility that DNA structures such as quadruplexes, within a largely duplex DNA background, could act as molecular anchors for locally induced epigenetic modifications.

G-quadruplexes formation in the 5'UTRs of mRNAs associated with colorectal cancer pathways

RNA G-quadruplexes (rG4) are stable non-canonical secondary structures composed of G-rich sequences. Many rG4 structures located in the 5'UTRs of mRNAs act as translation repressors due to their high stability which is thought to impede ribosomal scanning. That said, it is not known if these are mRNA-specific examples, or if they are indicative of a global expression regulation mechanism of the mRNAs involved in a common pathway based on structure folding recognition. Gene-ontology analysis of mRNAs bearing a predicted rG4 motif in their 5'UTRs revealed an enrichment for mRNAs associated with the colorectal cancer pathway. Bioinformatic tools for rG4 prediction, and experimental in vitro validations were used to confirm and compare the folding of the predicted rG4s of the mRNAs associated with dysregulated pathways in colorectal cancer. The rG4 folding was confirmed for the first time for 9 mRNAs. A repressive effect of 3 rG4 candidates on the expression of a reporter gene was also measured in colorectal cancer cell lines. This work highlights the fact that rG4 prediction is not yet accurate, and that experimental characterization is still essential in order to identify the precise rG4 folding sequences and the possible common features shared between the rG4 overrepresented in important biological pathways.

Motif independent identification of potential RNA G-quadruplexes by G4RNA screener

Motivation

G-quadruplex structures in RNA molecules are known to have regulatory impacts in cells but are difficult to locate in the genome. The minimal requirements for G-quadruplex folding in RNA (G_≥3N_1-7G_≥3N_1-7G_≥3N_1-7G_≥3) is being challenged by observations made on specific examples in recent years. The definition of potential G-quadruplex sequences has major repercussions on the observation of the structure since it introduces a bias. The canonical motif only describes a sub-population of the reported G-quadruplexes. To address these issues, we propose an RNA G-quadruplex prediction strategy that does not rely on a motif definition.

Results

We trained an artificial neural network with sequences of experimentally validated G-quadruplexes from the G4RNA database encoded using an abstract definition of their sequence. This artificial neural network, G4NN, evaluates the similarity of a given sequence to known G-quadruplexes and reports it as a score. G4NN has a predictive power comparable to the reported G richness and G/C skewness evaluations that are the current state-of-the-art for the identification of potential RNA G-quadruplexes. We combined these approaches in the G4RNA screener, a program designed to manage and evaluate the sequences to identify potential G-quadruplexes.

Availability and implementation

G4RNA screener is available for download at http://gitlabscottgroup.med.usherbrooke.ca/J-Michel/g4rna_screener.

Supplementary information

Supplementary data are available at Bioinformatics online.

Multiscale Analysis of Independent Alzheimer's Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus

Investigators have long suspected that pathogenic microbes might contribute to the onset and progression of Alzheimer's disease (AD) although definitive evidence has not been presented. Whether such findings represent a causal contribution, or reflect opportunistic passengers of neurodegeneration, is also difficult to resolve. We constructed multiscale networks of the late-onset AD-associated virome, integrating genomic, transcriptomic, proteomic, and histopathological data across four brain regions from human post-mortem tissue. We observed increased human herpesvirus 6A (HHV-6A) and human herpesvirus 7 (HHV-7) from subjects with AD compared with controls. These results were replicated in two additional, independent and geographically dispersed cohorts. We observed regulatory relationships linking viral abundance and modulators of APP metabolism, including induction of APBB2, APPBP2, BIN1, BACE1, CLU, PICALM, and PSEN1 by HHV-6A. This study elucidates networks linking molecular, clinical, and neuropathological features with viral activity and is consistent with viral activity constituting a general feature of AD.

G4RNA screener web server: User focused interface for RNA G-quadruplex prediction

Though RNA G-quadruplexes became a focus of study over a decade ago, the main challenge associated with the identification of new potential G-quadruplexes remains a bottleneck step. It slows the study of these non-canonical structures in nucleic acids, and thus the understanding of their significance. The G4RNA screener is an accurate tool for the prediction of RNA G-quadruplexes but its deployment has brought to light an issue with its accessibility to G-quadruplex experts and biologists. G4RNA screener web server is a platform that provides a much needed interface to manage the input, parameters and result display of the main command-line ready tool. It is accessible at http://scottgroup.med.usherbrooke.ca/G4RNA_screener/.

G-Quadruplexes influence pri-microRNA processing

RNA G-Quadruplexes (G4) have been shown to possess many biological functions, including the regulation of microRNA (miRNA) biogenesis and function. However, their impact on pri-miRNA processing remains unknown. We identified G4 located near the Drosha cleavage site in three distinct pri-miRNAs: pri-mir200c, pri-mir451a, and pri-mir497. The folding of the potential G4 motifs was determined in solution. Subsequently, mutations disrupting G4 folding led to important changes in the mature miRNAs levels in cells. Moreover, using small antisense oligonucleotides binding to the pri-miRNA, it was possible to modulate, either positively or negatively, the mature miRNA levels. Together, these data demonstrate that G4 motifs could contribute to the regulation of pri-mRNA processing, a novel role for G4. Considering that bio-informatics screening indicates that between 9% and 50% of all pri-miRNAs contain a putative G4, these structures possess interesting potential as future therapeutic targets.

RNA G-quadruplexes: emerging mechanisms in disease

RNA G-quadruplexes (G4s) are formed by G-rich RNA sequences in protein-coding (mRNA) and non-coding (ncRNA) transcripts that fold into a four-stranded conformation. Experimental studies and bioinformatic predictions support the view that these structures are involved in different cellular functions associated to both DNA processes (telomere elongation, recombination and transcription) and RNA post-transcriptional mechanisms (including pre-mRNA processing, mRNA turnover, targeting and translation). An increasing number of different diseases have been associated with the inappropriate regulation of RNA G4s exemplifying the potential importance of these structures on human health. Here, we review the different molecular mechanisms underlying the link between RNA G4s and human diseases by proposing several overlapping models of deregulation emerging from recent research, including (i) sequestration of RNA-binding proteins, (ii) aberrant expression or localization of RNA G4-binding proteins, (iii) repeat associated non-AUG (RAN) translation, (iv) mRNA translational blockade and (v) disabling of protein–RNA G4 complexes. This review also provides a comprehensive survey of the functional RNA G4 and their mechanisms of action. Finally, we highlight future directions for research aimed at improving our understanding on RNA G4-mediated regulatory mechanisms linked to diseases.

The super-n-motifs model: a novel alignment-free approach for representing and comparing RNA secondary structures

Motivation

Comparing ribonucleic acid (RNA) secondary structures of arbitrary size uncovers structural patterns that can provide a better understanding of RNA functions. However, performing fast and accurate secondary structure comparisons is challenging when we take into account the RNA configuration (i.e. linear or circular), the presence of pseudoknot and G-quadruplex (G4) motifs and the increasing number of secondary structures generated by high-throughput probing techniques. To address this challenge, we propose the super-n-motifs model based on a latent analysis of enhanced motifs comprising not only basic motifs but also adjacency relations. The super-n-motifs model computes a vector representation of secondary structures as linear combinations of these motifs.

Results

We demonstrate the accuracy of our model for comparison of secondary structures from linear and circular RNA while also considering pseudoknot and G4 motifs. We show that the super-n-motifs representation effectively captures the most important structural features of secondary structures, as compared to other representations such as ordered tree, arc-annotated and string representations. Finally, we demonstrate the time efficiency of our model, which is alignment free and capable of performing large-scale comparisons of 10 000 secondary structures with an efficiency up to 4 orders of magnitude faster than existing approaches.

Availability and Implementation

The super-n-motifs model was implemented in C ++. Source code and Linux binary are freely available at http://jpsglouzon.github.io/supernmotifs/ .

Contact

Shengrui.Wang@Usherbrooke.ca.

Supplementary information

Supplementary data are available at Bioinformatics o nline.

RNA G-quadruplexes are globally unfolded in eukaryotic cells and depleted in bacteria

In vitro, some RNAs can form stable four-stranded structures known as G-quadruplexes. Although RNA G-quadruplexes have been implicated in posttranscriptional gene regulation and diseases, direct evidence for their formation in cells has been lacking. Here, we identified thousands of mammalian RNA regions that can fold into G-quadruplexes in vitro, but in contrast to previous assumptions, these regions were overwhelmingly unfolded in cells. Model RNA G-quadruplexes that were unfolded in eukaryotic cells were folded when ectopically expressed in Escherichia coli; however, they impaired translation and growth, which helps explain why we detected few G-quadruplex-forming regions in bacterial transcriptomes. Our results suggest that eukaryotes have a robust machinery that globally unfolds RNA G-quadruplexes, whereas some bacteria have instead undergone evolutionary depletion of G-quadruplex-forming sequences.