Motivation: Recent technological advances revealed that an unexpected large number of proteins interact with transcripts even if the RNA-binding domains are not annotated. We introduce catRAPID signature to identify ribonucleoproteins based on physico-chemical features instead of sequence similarity searches. The algorithm, trained on human proteins and tested on model organisms, calculates the overall RNA-binding propensity followed by the prediction of RNA-binding regions. catRAPID signature outperforms other algorithms in the identification of RNA-binding proteins and detection of non-classical RNA-binding regions. Results are visualized on a webpage and can be downloaded or forwarded to catRAPID omics for predictions of RNA targets. Availability and implementation: catRAPID signature can be accessed at http://s.tartaglialab.com/new_submission/signature. Contact: gian.tartaglia@crg.es or gian@tartaglialab.com Supplementary information: Supplementary data are available at Bioinformatics online.

SummaryRecent evidence indicates that specific RNAs promote the formation of ribonucleoprotein condensates by acting as scaffolds for RNA-binding proteins (RBPs). We systematically investigated RNA-RBP interaction networks to understand ribonucleoprotein assembly. We found that highly contacted RNAs are structured, have long UTRs, and contain nucleotide repeat expansions. Among the RNAs with such properties, we identified the FMR1 3′ UTR that harbors CGG expansions implicated in fragile X-associated tremor/ataxia syndrome (FXTAS). We studied FMR1 binding partners in silico and in vitro and prioritized the splicing regulator TRA2A for further characterization. In a FXTAS cellular model, we validated the TRA2A-FMR1 interaction and investigated implications of its sequestration at both transcriptomic and post-transcriptomic levels. We found that TRA2A co-aggregates with FMR1 in a FXTAS mouse model and in post-mortem human samples. Our integrative study identifies key components of ribonucleoprotein aggregates, providing links to neurodegenerative disease and allowing the discovery of therapeutic targets.

Here we introduce the Computational Recognition of Secondary Structure (CROSS) method to calculate the structural profile of an RNA sequence (single- or double-stranded state) at single-nucleotide resolution and without sequence length restrictions. We trained CROSS using data from high-throughput experiments such as Selective 2΄-Hydroxyl Acylation analyzed by Primer Extension (SHAPE; Mouse and HIV transcriptomes) and Parallel Analysis of RNA Structure (PARS; Human and Yeast transcriptomes) as well as high-quality NMR/X-ray structures (PDB database). The algorithm uses primary structure information alone to predict experimental structural profiles with >80% accuracy, showing high performances on large RNAs such as Xist (17 900 nucleotides; Area Under the ROC Curve AUC of 0.75 on dimethyl sulfate (DMS) experiments). We integrated CROSS in thermodynamics-based methods to predict secondary structure and observed an increase in their predictive power by up to 30%.