RNA interaction format: a general data format for RNA interactions.

SUMMARY: RNA molecules play crucial roles in various biological processes. They mediate their function mainly by interacting with other RNAs or proteins. At present, information about these interactions is distributed over different resources, often providing the data in simple tab-delimited formats that differ between the databases. There is no standardized data format that can capture the nature of all these different interactions in detail. AVAILABILITY AND IMPLEMENTATION: Here, we propose the RNA interaction format (RIF) for the detailed representation of RNA-RNA and RNA-Protein interactions and provide reference implementations in C/C++, Python, and JavaScript. RIF is released under licence GNU General Public License version 3 (GNU GPLv3) and is available on https://github.com/RNABioInfo/rna-interaction-format.

Peakhood: individual site context extraction for CLIP-seq peak regions.

MOTIVATION: CLIP-seq is by far the most widely used method to determine transcriptome-wide binding sites of RNA-binding proteins (RBPs). The binding site locations are identified from CLIP-seq read data by tools termed peak callers. Many RBPs bind to a spliced RNA (i.e. transcript) context, but all currently available peak callers only consider and report the genomic context. To accurately model protein binding behavior, a tool is needed for the individual context assignment to CLIP-seq peak regions. RESULTS: Here we present Peakhood, the first tool that utilizes CLIP-seq peak regions identified by peak callers, in tandem with CLIP-seq read information and genomic annotations, to determine which context applies, individually for each peak region. For sites assigned to transcript context, it further determines the most likely splice variant, and merges results for any number of datasets to obtain a comprehensive collection of transcript context binding sites. AVAILABILITY AND IMPLEMENTATION: Peakhood is freely available under MIT license at: https://github.com/BackofenLab/Peakhood. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Distinct SARS-CoV-2 RNA fragments activate Toll-like receptors 7 and 8 and induce cytokine release from human macrophages and microglia.

Introduction: The pandemic coronavirus disease 19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is marked by thromboembolic events and an inflammatory response throughout the body, including the brain. Methods: Employing the machine learning approach BrainDead we systematically screened for SARS-CoV-2 genome-derived single-stranded (ss) RNA fragments with high potential to activate the viral RNA-sensing innate immune receptors Toll-like receptor (TLR)7 and/or TLR8. Analyzing HEK TLR7/8 reporter cells we tested such RNA fragments with respect to their potential to induce activation of human TLR7 and TLR8 and to activate human macrophages, as well as iPSC-derived human microglia, the resident immune cells in the brain. Results: We experimentally validated several sequence-specific RNA fragment candidates out of the SARS-CoV-2 RNA fragments predicted in silico as activators of human TLR7 and TLR8. Moreover, these SARS-CoV-2 ssRNAs induced cytokine release from human macrophages and iPSC-derived human microglia in a sequence- and species-specific fashion. Discussion: Our findings determine TLR7 and TLR8 as key sensors of SARS-CoV-2-derived ssRNAs and may deepen our understanding of the mechanisms how this virus triggers, but also modulates an inflammatory response through innate immune signaling.