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≥3N1-7 G≥3N1-7 G≥3N1-7 G≥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. CONTACT: [email protected] or [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics o nline.

SUMMARY: Discovering function-related structural features, such as the cloverleaf shape of transfer RNA secondary structures, is essential to understand RNA function. With this aim, we have developed a platform, named Structurexplor, to facilitate the exploration of structural features in populations of RNA secondary structures. It has been designed and developed to help biologists interactively search for, evaluate and select interesting structural features that can potentially explain RNA functions. AVAILABILITY AND IMPLEMENTATION: Structurxplor is a web application available at http://structurexplor.dinf.usherbrooke.ca. The source code can be found at http://jpsglouzon.github.io/structurexplor/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: RNA-dependent gene silencing is becoming a routine tool used in laboratories worldwide. One of the important remaining hurdles in the selection of the target sequence, if not the most important one, is the designing of tools that have minimal off-target effects (i.e. cleaves only the desired sequence). Increasingly, in the current dawn of the post-genomic era, there is a heavy reliance on tools that are suitable for high-throughput functional genomics, consequently more and more bioinformatic software is becoming available. However, to date none have been designed to satisfy the ever-increasing need for the accurate selection of targets for a specific silencing reagent. RESULTS: In order to overcome this hurdle we have developed RiboSubstrates http://www.riboclub.org/ribosubstrates. This integrated bioinformatic software permits the searching of a cDNA database for all potential substrates for a given ribozyme. This includes the mRNAs that perfectly match the specific requirements of a given ribozyme, as well those including Wobble base pairs and mismatches. The results generated allow rapid selection of sequences suitable as targets for RNA degradation. The current web-based RiboSubstrates version permits the identification of potential gene targets for both SOFA-HDV ribozymes and for hammerhead ribozymes. Moreover, a minimal template for the search of siRNAs is also available. This flexible and reliable tool is easily adaptable for use with any RNA tool (i.e. other ribozymes, deoxyribozymes and antisense), and may use the information present in any cDNA bank. CONCLUSION: RiboSubstrates should become an essential step for all, even including "non-RNA biologists", who endeavor to develop a gene-inactivation system.