Irmtraud M. Meyer

RNA–RNA interactions (RRI) can now be probed on a transcriptome-wide scale using proximity ligation methods such as SPLASH, PARIS, LIGR-seq, RIC-seq, and others. While individual pipelines for computationally processing the corresponding raw duplex reads exist, there is currently no method for comparing and exploring RRI networks across experimental protocols and cellular conditions. It thus remains difficult to discover biologically relevant interactions, to assess reproducibility, and to evaluate protocol-specific differences. Here, we present InteRRact - a web server for interactively exploring human RRI datasets derived from published duplex probing experiments. One key feature is that all available datasets have been processed uniformly. InteRRact visualizes intermolecular RRIs as gene-level networks and intramolecular interactions as linear, locus-specific genomic tracks. All RRIs are annotated with multiple quantitative metrics and evaluated statistically, allowing the user to readily filter interactions by strength of evidence and confidence. Moreover, any two datasets can be compared to identify shared and dataset-specific interactions across cell types, conditions, and experimental protocols. InteRRact thereby enables the discovery of biologically relevant interactions. InteRRact is available at https://e-rna.org/interract.

For a few years, it has been possible to experimentally probe the universe of cis and trans RNA–RNA interactions in a transcriptome-wide manner. These experiments give rise to so-called duplex data, i.e. short reads generated via high-throughput sequencing that each encode information on a cis or trans RNA–RNA interaction. These raw duplex data require complex, subsequent computational analyses in order to be interpreted as solid evidence for actual cis and trans RNA–RNA interactions. While several methods have already been proposed to tackle this challenge, almost all of them lack one or more desirable feature—computational efficiency, ability to readily alter the main processing steps and parameter values, p-value estimation for predictions, and interoperability with the common bioinformatics tools for transcriptomics. To overcome these challenges, we present DuplexDiscoverer—a computational method and R package that allows for the efficient, adjustable, and conceptually coherent analysis of duplex data. DuplexDiscoverer is readily adaptable to analysing data from different experimental protocols and its results seamlessly integrate with the most commonly used bioinformatics tools for transcriptomics in R. Most importantly, DuplexDiscoverer generates predictions that are of superior or comparable quality to those of the existing methods while significantly improving time and memory efficiency.

E-RNA is a collection of web-servers for the prediction and visualisation of RNA secondary structures and their functional features, including in particular RNA–RNA interactions. In this updated version, we have added novel tools for RNA secondary structure prediction and have significantly updated the visualisation functionality. The new method CoBold can identify transient RNA structure features and their potential functional effects on a known RNA structure during co-transcriptional structure formation. New tool ShapeSorter can predict evolutionarily conserved RNA secondary structure features while simultaneously taking experimental SHAPE probing evidence into account. The web-server R-Chie which visualises RNA secondary structure information in terms of arc diagrams, can now be used to also visualise and intuitively compare RNA–RNA, RNA–DNA and DNA–DNA interactions alongside multiple sequence alignments and quantitative information. The prediction generated by any method in e-RNA can be readily visualised on the web-server. For completed tasks, users can download their results and readily visualise them later on with R-Chie without having to re-run the predictions. e-RNA can be found at http://www.e-rna.org.

Splicing is one key mechanism determining the state of any eukaryotic cell. Apart from linear splice variants, circular splice variants (circRNAs) can arise via non-canonical splicing involving a back-splice junction (BSJ). Most existing methods only identify circRNAs via the corresponding BSJ, but do not aim to estimate their full sequence identity or to identify different, alternatively spliced circular isoforms arising from the same BSJ. We here present CYCLeR, the first computational method for identifying the full sequence identity of new and alternatively spliced circRNAs and their abundances while simultaneously co-estimating the abundances of known linear splicing isoforms. We show that CYCLeR significantly outperforms existing methods in terms of F score and quantification of transcripts in simulated data. In a in a comparative study with long-read data, we also show the advantages of CYCLeR compared to existing methods. When analysing Drosophila melanogaster data, CYCLeR uncovers biological patterns of circRNA expression that other methods fail to observe.

Viruses are the cause of a considerable burden to human, animal and plant health, while on the other hand playing an important role in regulating entire ecosystems. The power of new sequencing technologies combined with new tools for processing “Big Data” offers unprecedented opportunities to answer fundamental questions in virology. Virologists have an urgent need for virus-specific bioinformatics tools. These developments have led to the formation of the European Virus Bioinformatics Center, a network of experts in virology and bioinformatics who are joining forces to enable extensive exchange and collaboration between these research areas. The EVBC strives to provide talented researchers with a supportive environment free of gender bias, but the gender gap in science, especially in math-intensive fields such as computer science, persists. To bring more talented women into research and keep them there, we need to highlight role models to spark their interest, and we need to ensure that female scientists are not kept at lower levels but are given the opportunity to lead the field. Here we showcase the work of the EVBC and highlight the achievements of some outstanding women experts in virology and viral bioinformatics.

There is an increased interest in the determination of RNA structures in vivo as it is now possible to probe them in a high-throughput manner, e.g. using SHAPE protocols. By now, there exist a range of computational methods that integrate experimental SHAPE-probing evidence into computational RNA secondary structure prediction. The state-of-the-art in this field is currently provided by computational methods that employ the minimum-free energy strategy for prediction RNA secondary structures with SHAPE-probing evidence. These methods, however, rely on the assumption that transcripts in vivo fold into the thermodynamically most stable configuration and ignore evolutionary evidence for conserved RNA structure features. We here present a new computational method, ShapeSorter, that predicts RNA structure features without employing the thermodynamic strategy. Instead, ShapeSorter employs a fully probabilistic framework to identify RNA structure features that are supported by evolutionary and SHAPE-probing evidence. Our method can capture RNA structure heterogeneity, pseudo-knotted RNA structures as well as transient and mutually exclusive RNA structure features. Moreover, it estimates P-values for the predicted RNA structure features which allows for easy filtering and ranking. We investigate the merits of our method in a comprehensive performance benchmarking and conclude that ShapeSorter has a significantly superior performance for predicting base-pairs than the existing state-of-the-art methods.

State-of-the-art methods for predicting novel trans RNA-RNA interactions use the so-called accessibility as key concept. It estimates whether a region in a given RNA sequence is accessible for forming trans interactions, using a thermodynamic model which quantifies its secondary structure features. RNA-RNA interactions are then predicted by finding the minimum free energy base-pairing between the two transcripts, taking into account the accessibility as energy penalty. We investigated the underlying assumptions of this approach using the two methods RNA plex and I nta RNA on two datasets, containing sRNA-mRNA and snoRNA-rRNA interactions, respectively. We find that (1) known trans RNA-RNA interactions frequently overlap regions containing RNA structure features, (2) the estimated accessibility reflects sRNA structures fairly well, but often disagrees with structure annotations of longer transcripts, (3) the prediction performance of RNA-RNA interaction prediction methods is independent of the quality of the estimated accessibility profiles, and (4) one important overall effect of accessibility profiles is to prevent the thermodynamic model from predicting too long interactions. Based on our findings, we conclude that the accessibility concept to the minimum free energy approach to predicting novel RNA-RNA interactions has conceptual limitations and discuss potential ways of improving the field in the future.

Splicing is one key mechanism determining the state of any eukaryotic cell. Apart from linear splice variants, circular splice variants ( circRNA s) can arise via non-canonical splicing involving a back-splice junction (BSJ). Most existing methods only identify circRNA s via the corresponding BSJ, but do not aim to estimate their full sequence identity or to identify different, alternatively spliced circular isoforms arising from the same BSJ. We here present CYCL e R, the first computational method for identifying the full sequence identify of new and alternatively spliced circRNA s and their abundances while simultaneously co-estimating the abundances of known linear splicing isoforms. We show that CYCL e R significantly out-performs existing methods in terms of sensitivity, precision and quantification of transcripts. When analysing D. melanogaster data, CYCL e R uncovers biological patterns of circRNA expression that other methods fail to observe.

The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8–9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.

RNA structure formation in vivo happens co-transcriptionally while the transcript is being made. The corresponding co-transcriptional folding pathway typically involves transient RNA structure features that are not part of the final, functional RNA structure. These transient features can play important functional roles of their own and also influence the formation of the final RNA structure in vivo. We here present CoBold, a computational method for identifying different functional classes of transient RNA structure features that can either aid or hinder the formation of a known reference RNA structure. Our method takes as input either a single RNA or a corresponding multiple-sequence alignment as well as a known reference RNA secondary structure and identifies different classes of transient RNA structure features that could aid or prevent the formation of the given RNA structure. We make CoBold available via a web-server which includes dedicated data visualisation.