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Protein–RNA interactions are at the heart of cell regulation. From transcription, processing, storage, and translation, all the stages in the life cycle of an RNA depend on interactions with proteins. Although technologies are making remarkable progress in unraveling the landscape of protein–RNA interactions, many key issues are unclear. We still have to identify how many proteins have RNA-binding ability, what are their targets and functional pathways. Moreover, while we know the number of protein-coding genes in the human genome, functional non-coding RNAs are still poorly defined. What is the function of the non-coding part of the eukaryotic transcriptome? A clear understanding of the biological functions of coding and non-coding transcripts would provide novel insights in molecular biology. What are the protein components binding to an RNA while it is being produced? Our lack of understanding of how ribonucleoprotein complexes assemble is a major rate-limiting factor to future progress in the field. We need to generate an in-depth characterization of protein–RNA complexes that form in cells during development and in response to external stimuli. ; My research received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (GT), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P). I also acknowledge support from AGAUR (2014 SGR 00685), the Spanish Ministry of Economy and Competitiveness, "Centro de Excelencia Severo Ochoa 2013–2017" (SEV-2012-0208).

SUMMARY: Here we introduce omiXcore, a server for calculations of protein binding to large RNAs (> 500 nucleotides). Our webserver allows (i) use of both protein and RNA sequences without size restriction, (ii) pre-compiled library for exploration of human long intergenic RNAs interactions and (iii) prediction of binding sites. RESULTS: omiXcore was trained and tested on enhanced UV Cross-Linking and ImmunoPrecipitation data. The method discriminates interacting and non-interacting protein-RNA pairs and identifies RNA binding sites with Areas under the ROC curve > 0.80, which suggests that the tool is particularly useful to prioritize candidates for further experimental validation. AVAILABILITY AND IMPLEMENTATION: omiXcore is freely accessed on the web at http://service.tartaglialab.com/grant_submission/omixcore. CONTACT: gian.tartaglia@crg.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. ; We acknowledge support of the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017' and the CERCA Programme / Generalitat de Catalunya. This work was supported by the European Union Seventh Framework Programme [FP7/2007-13], European Research Council RIBOMYLOME_309545 (Gian Gaetano Tartaglia) and Spanish Ministry of Economy and Competitiveness BFU2014-5505-P (Gian Gaetano Tartaglia)

Fragile X-associated tremor/ataxia syndrome (FXTAS) is an inherited neurodegenerative disorder manifesting in carriers of 55 to 200 CGG repeats in the 5' untranslated region (UTR) of the fragile X mental retardation gene (FMR1). FXTAS is characterized by enhanced FMR1 transcription and the accumulation of CGG repeat-containing FMR1 messenger RNA in nuclear foci, while the FMRP protein expression levels remain normal or moderately low. The neuropathological hallmark in FXTAS is the presence of intranuclear, ubiquitin-positive inclusions that also contain FMR1 transcript. Yet, the complete protein complement of FXTAS inclusions and the molecular events that trigger neuronal death in FXTAS remain unclear. In this review, we present the two most accepted toxicity mechanisms described so far, namely RNA gain-of-function and protein gain-of-function by means of repeat-associated non-AUG translation, and discuss current experimental and computational strategies to better understand FXTAS pathogenesis. Finally, we review the current perspectives for drug development with disease-modifying potential for FXTAS. ; Our research received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_ 309545 (Gian Gaetano Tartaglia), and from the Fundació La Marató de TV3 (20142731). We also acknowledge support from the Spanish Ministry of Economy and Competitiveness (BFU2011-26206 and BFU2014-55054-P) and "Centro de Excelencia Severo Ochoa 2013– 2017" (SEV-2012-0208)

Access to genome-wide data provides the opportunity to address questions concerning the ability of transcription factors (TFs) to assemble in distinct macromolecular complexes. Here, we introduce the PAnDA (Protein And DNA Associations) approach to characterize DNA associations with human TFs using expression profiles, protein-protein interactions and recognition motifs. Our method predicts TF binding events with >0.80 accuracy revealing cell-specific regulatory patterns that can be exploited for future investigations. Even when the precise DNA-binding motifs of a specific TF are not available, the information derived from protein-protein networks is sufficient to perform high-confidence predictions (area under the ROC curve of 0.89). PAnDA is freely available at http://service.tartaglialab.com/new_submission/panda. ; The research leading to these results has received funding from the European Research Council under the European/nUnion's Seventh Framework Programme (FP7/2007-2013)/ ERC grant agreementRIBOMYLOME 309545; the Fundació La Marató de TV3 (20142731); and the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017' (SEV-2012-0208). Funding for Open Access charge: European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement RIBOMYLOME 309545 and Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017' (SEV-2012-0208).

Bacterial infections have been on the rise world-wide in recent years and have a considerable impact on human well-being in terms of attributable deaths and disability-adjusted life years. Yet many mechanisms underlying bacterial pathogenesis are still poorly understood. Here, we introduce the BacFITBase database for the systematic characterization of bacterial proteins relevant for host infection aimed to enable the identification of new antibiotic targets. BacFITBase is manually curated and contains more than 90 000 entries with information on the contribution of individual genes to bacterial fitness under in vivo infection conditions in a range of host species. The data were collected from 15 different studies in which transposon mutagenesis was performed, including top-priority pathogens such as Acinetobacter baumannii and Campylobacter jejuni, for both of which increasing antibiotic resistance has been reported. Overall, BacFITBase includes information on 15 pathogenic bacteria and 5 host vertebrates across 10 different tissues. It is freely available at www.tartaglialab.com/bacfitbase. ; This study was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades [SAF2015-72518-EXP, SAF2017-82158-R, RYC-2012-09999]; European Research Council [RIBOMYLOME_309545]; European Union's Horizon 2020 Research and Innovation Programme [727658, IASIS]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P, BFU2017-86970-P]; Spanish Ministry of Economy and Competitiveness; 'Centro de Excelencia Severo Ochoa 2013–2017'; CERCA Programme of the Generalitat de Catalunya; European Union's Horizon 2020 Research and Innovation Programme, Marie Sklodowska-Curie Individual Fellowship [793135, 'DeepRNA' to B.L.]; European Society of Clinical Microbiology and Infectious Diseases, Research Grant 2016 (ESCMID). Funding for open access charge: Spanish Ministerio de Ciencia, Innovación y Universidades [SAF2017-82158-R]

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP. ; The research leading to this work has been supported by European Research Council (RIBOMYLOME_309545 and ASTRA_855923), by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754490 and under projects IASIS_727658 and INFORE_825080, by the Spanish Ministry of Economy and Competitiveness BFU2017-86970-P as well as the collaboration with Peter St. George-Hyslop financed by the Wellcome Trust.

It has been reported that genes up-regulated in cancer are often down-regulated in neurodegenerative disorders and vice versa. The fact that apparently unrelated diseases share functional pathways suggests a link between their etiopathogenesis and the properties of molecules involved. Are there specific features that explain the exclusive association of proteins with either cancer or neurodegeneration? We performed a large-scale analysis of physico-chemical properties to understand what characteristics differentiate classes of diseases. We found that structural disorder significantly distinguishes proteins up-regulated in neurodegenerative diseases from those linked to cancer. We also observed high correlation between structural disorder and age of onset in Frontotemporal Dementia, Parkinson's and Alzheimer's diseases, which strongly supports the role of protein unfolding in neurodegenerative processes. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Fundació La Marató de TV3 (20142731). We also acknowledge support from AGAUR (2014 SGR00685) and the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P), 'Centro de Excelencia Severo Ochoa 2013-2017′ (SEV-2012-0208)

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%. ; The research leading to these results has received funding from European Union Seventh Framework Programme [FP7/2007-2013]; European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]; AGAUR [2014 SGR 00685 to GGT]; Spanish Ministry of Economy and Competitiveness, European Research Development Fund ERDF, 'Centro de Excelencia Severo Ochoa 2013-2017' [SEV-2012-0208]. Funding for open access charge: European Research Council [RIBOMYLOME_309545 to GGT]; Spanish Ministry of Economy and Competitiveness [BFU2014-55054-P to GGT]. The authors also thank the CRG fellowship to SM.

To compare the secondary structure profiles of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure profile at single-nucleotide resolution and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. CROSSalign performs pair-wise comparisons and is able to find homologs between thousands of matches identifying the exact regions of similarity between profiles of different lengths. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modeling. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/crossalign. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P and BFU2017-86970-P). We also acknowledge support from AGAUR (2014 SGR 00685), the Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa 2013–2017 (SEV-2012-0208). We also thank the CRG fellowship to SM.

MOTIVATION: RNA structure is difficult to predict in vivo due to interactions with enzymes and other molecules. Here we introduce CROSSalive, an algorithm to predict the single- and double-stranded regions of RNAs in vivo using predictions of protein interactions. RESULTS: Trained on icSHAPE data in presence (m6a+) and absence of N6 methyladenosine modification (m6a-), CROSSalive achieves cross-validation accuracies between 0.70 and 0.88 in identifying high-confidence single- and double-stranded regions. The algorithm was applied to the long non-coding RNA Xist (17 900 nt, not present in the training) and shows an Area under the ROC curve of 0.83 in predicting structured regions. AVAILABILITY AND IMPLEMENTATION: CROSSalive webserver is freely accessible at http://service.tartaglialab.com/new_submission/crossalive. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. ; The research leading to these results has received funding from European Research Council RIBOMYLOME_309545, European Union's Horizon 2020 IASIS_727658 and INFORE_825070, as well as Spanish Ministry of Economy and Competitiveness BFU2017-86970-P

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a late-onset neurodegenerative monogenetic disorder affecting carriers of premutation (PM) forms of the FMR1 gene, resulting in a progressive development of tremors, ataxia, and neuropsychological problems. This highly disabling disease is quite common in the general population with an estimation of about 20 million PM carriers worldwide. The chances of developing FXTAS increase dramatically with age, with about 45% of male carriers over the age of 50 being affected. Both the gene and pathogenic trigger, a mutant expansion of CGG RNA, causing FXTAS are known. This makes it an interesting disease to develop targeted therapeutic interventions for. Yet, no such interventions are available at this moment. Here we discuss in silico, in vitro, and in vivo approaches and how they have been used to identify the molecular determinants of FXTAS pathology. These approaches have yielded substantial information about FXTAS pathology and, consequently, many markers have emerged to play a key role in understanding the disease mechanism. Integration of the different approaches is expected to provide crucial information about the value of these markers as either therapeutic target or biomarker, essential to monitor therapeutic interventions in the future. ; The research leading to these results has been supported by the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013) ERC grant agreement RIBOMYLOME_309545 to GT and ASTRA 855923 to GT, the Spanish Ministry of Science and Innovation (AEI/ERDF, BFU2014-55054-P and BFU2017-86970-P) and the 'Fundació La Marató de TV3' (PI043296)

Changes in the abundance of protein and RNA molecules can impair the formation of complexes in the cell leading to toxicity and death. Here we exploit the information contained in protein, RNA and DNA interaction networks to provide a comprehensive view of the regulation layers controlling the concentration-dependent formation of assemblies in the cell. We present the emerging concept that RNAs can act as scaffolds to promote the formation ribonucleoprotein complexes and coordinate the post-transcriptional layer of gene regulation. We describe the structural and interaction network properties that characterize the ability of protein and RNA molecules to interact and phase separate in liquid-like compartments. Finally, we show that presence of structurally disordered regions in proteins correlate with the propensity to undergo liquid-to-solid phase transitions and cause human diseases. Also see the video abstract here https://youtu.be/kfpqibsNfS0. ; The research leading to these results has been supported by European Research Council (RIBOMYLOME_309545 and ASTRA_855923), the H2020 projects IASIS_727658 and INFORE_825080, the Spanish Ministry of Economy and Competitiveness BFU2017‐86970‐P, the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska‐Curie grant agreement No 754490 within the MINDED project.We also acknowledge support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes. ; The research leading to these results has been supported by European Research Council grant agreements RIBOMYLOME (309545) and ASTRA (855923), and the European Union's Horizon 2020 research and innovation programme grant agreements IASIS (727658), DeepRNA (793135), and INFORE (825080). We would like to thank for the financial support the project Grant SysBioNet, Italian Roadmap Research Infrastructures 2012.

BACKGROUND: The large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites. RESULTS: Here, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature. CONCLUSIONS: /nSeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545, and from the Spanish Ministry of Economy and Competitiveness/n(SAF2011-26211). We also acknowledge support from the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' (SEV-2012-0208)

BACKGROUND: Comparison between multiple protein datasets requires the choice of an appropriate reference system and a number of variables to describe their differences. Here we introduce an innovative approach to discriminate multiple protein datasets (multiCM) and to measure enrichments in gene ontology terms (cleverGO) using semantic similarities. RESULTS: We illustrate the powerfulness of our approach by investigating the links between RNA-binding ability and other protein features, such as structural disorder and aggregation, in S. cerevisiae, C. elegans, M. musculus and H. sapiens. Our results are in striking agreement with available experimental evidence and unravel features that are key to understand the mechanisms regulating cellular homeostasis. CONCLUSIONS: In an intuitive way, multiCM and cleverGO provide accurate classifications of physico-chemical features and annotations of biological processes, molecular functions and cellular components, which is extremely useful for the discovery and characterization of new trends in protein datasets. The multiCM and cleverGO can be freely accessed on the Web at http://www.tartaglialab.com/cs_multi/submission and http://www.tartaglialab.com/GO_analyser/universal . Each of the pages contains links to the corresponding documentation and tutorial. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P). We also acknowledge support from AGAUR (2014 SGR 00685), the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' (SEV-2012-0208). PK and RDP are recipients of "La Caixa" and "Severo Ochoa" studentships, respectively