IRSOM2: a web server for predicting bifunctional RNAs.

Recent advances have shown that some biologically active non-coding RNAs (ncRNAs) are actually translated into polypeptides that have a physiological function as well. This paradigm shift requires adapted computational methods to predict this new class of 'bifunctional RNAs'. Previously, we developed IRSOM, an open-source algorithm to classify non-coding and coding RNAs. Here, we use the binary statistical model of IRSOM as a ternary classifier, called IRSOM2, to identify bifunctional RNAs as a rejection of the two other classes. We present its easy-to-use web interface, which allows users to perform predictions on large datasets of RNA sequences in a short time, to re-train the model with their own data, and to visualize and analyze the classification results thanks to the implementation of self-organizing maps (SOM). We also propose a new benchmark of experimentally validated RNAs that play both protein-coding and non-coding roles, in different organisms. Thus, IRSOM2 showed promising performance in detecting these bifunctional transcripts among ncRNAs of different types, such as circRNAs and lncRNAs (in particular those of shorter lengths). The web server is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

LncACTdb 3.0: an updated database of experimentally supported ceRNA interactions and personalized networks contributing to precision medicine.

LncACTdb 3.0 is a comprehensive database of experimentally supported interactions among competing endogenous RNA (ceRNA) and the corresponding personalized networks contributing to precision medicine. LncACTdb 3.0 is freely available at http://bio-bigdata.hrbmu.edu.cn/LncACTdb or http://www.bio-bigdata.net/LncACTdb. We have updated the LncACTdb 3.0 database with several new features, including (i) 5669 experimentally validated ceRNA interactions across 25 species and 537 diseases/phenotypes through manual curation of published literature, (ii) personalized ceRNA interactions and networks for 16 228 patients from 62 datasets in TCGA and GEO, (iii) sub-cellular and extracellular vesicle locations of ceRNA manually curated from literature and data sources, (iv) more than 10 000 experimentally supported long noncoding RNA (lncRNA) biomarkers associated with tumor diagnosis and therapy, and (v) lncRNA/mRNA/miRNA expression profiles with clinical and pathological information of thousands of cancer patients. A panel of improved tools has been developed to explore the effects of ceRNA on individuals with specific pathological backgrounds. For example, a network tool provides a comprehensive view of lncRNA-related, patient-specific, and custom-designed ceRNA networks. LncACTdb 3.0 will provide novel insights for further studies of complex diseases at the individual level and will facilitate the development of precision medicine to treat such diseases.

RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility.

Establishing an RNA-associated interaction repository facilitates the system-level understanding of RNA functions. However, as these interactions are distributed throughout various resources, an essential prerequisite for effectively applying these data requires that they are deposited together and annotated with confidence scores. Hence, we have updated the RNA-associated interaction database RNAInter (RNA Interactome Database) to version 4.0, which is freely accessible at http://www.rnainter.org or http://www.rna-society.org/rnainter/. Compared with previous versions, the current RNAInter not only contains an enlarged data set, but also an updated confidence scoring system. The merits of this 4.0 version can be summarized in the following points: (i) a redefined confidence scoring system as achieved by integrating the trust of experimental evidence, the trust of the scientific community and the types of tissues/cells, (ii) a redesigned fully functional database that enables for a more rapid retrieval and browsing of interactions via an upgraded user-friendly interface and (iii) an update of entries to >47 million by manually mining the literature and integrating six database resources with evidence from experimental and computational sources. Overall, RNAInter will provide a more comprehensive and readily accessible RNA interactome platform to investigate the regulatory landscape of cellular RNAs.

RPS: a comprehensive database of RNAs involved in liquid-liquid phase separation.

Liquid-liquid phase separation (LLPS) is critical for assembling membraneless organelles (MLOs) such as nucleoli, P-bodies, and stress granules, which are involved in various physiological processes and pathological conditions. While the critical role of RNA in the formation and the maintenance of MLOs is increasingly appreciated, there is still a lack of specific resources for LLPS-related RNAs. Here, we presented RPS (http://rps.renlab.org), a comprehensive database of LLPS-related RNAs in 20 distinct biomolecular condensates from eukaryotes and viruses. Currently, RPS contains 21,613 LLPS-related RNAs with three different evidence types, including 'Reviewed', 'High-throughput' and 'Predicted'. RPS provides extensive annotations of LLPS-associated RNA properties, including sequence features, RNA structures, RNA-protein/RNA-RNA interactions, and RNA modifications. Moreover, RPS also provides comprehensive disease annotations to help users to explore the relationship between LLPS and disease. The user-friendly web interface of RPS allows users to access the data efficiently. In summary, we believe that RPS will serve as a valuable platform to study the role of RNA in LLPS and further improve our understanding of the biological functions of LLPS.

RNAPhaSep: a resource of RNAs undergoing phase separation.

Liquid-liquid phase separation (LLPS) partitions cellular contents, underlies the formation of membraneless organelles and plays essential biological roles. To date, most of the research on LLPS has focused on proteins, especially RNA-binding proteins. However, accumulating evidence has demonstrated that RNAs can also function as 'scaffolds' and play essential roles in seeding or nucleating the formation of granules. To better utilize the knowledge dispersed in published literature, we here introduce RNAPhaSep (http://www.rnaphasep.cn), a manually curated database of RNAs undergoing LLPS. It contains 1113 entries with experimentally validated RNA self-assembly or RNA and protein co-involved phase separation events. RNAPhaSep contains various types of information, including RNA information, protein information, phase separation experiment information and integrated annotation from multiple databases. RNAPhaSep provides a valuable resource for exploring the relationship between RNA properties and phase behaviour, and may further enhance our comprehensive understanding of LLPS in cellular functions and human diseases.

Single-cell quantification of a broad RNA spectrum reveals unique noncoding patterns associated with cell types and states.

The ability to interrogate total RNA content of single cells would enable better mapping of the transcriptional logic behind emerging cell types and states. However, current single-cell RNA-sequencing (RNA-seq) methods are unable to simultaneously monitor all forms of RNA transcripts at the single-cell level, and thus deliver only a partial snapshot of the cellular RNAome. Here we describe Smart-seq-total, a method capable of assaying a broad spectrum of coding and noncoding RNA from a single cell. Smart-seq-total does not require splitting the RNA content of a cell and allows the incorporation of unique molecular identifiers into short and long RNA molecules for absolute quantification. It outperforms current poly(A)-independent total RNA-seq protocols by capturing transcripts of a broad size range, thus enabling simultaneous analysis of protein-coding, long-noncoding, microRNA, and other noncoding RNA transcripts from single cells. We used Smart-seq-total to analyze the total RNAome of human primary fibroblasts, HEK293T, and MCF7 cells, as well as that of induced murine embryonic stem cells differentiated into embryoid bodies. By analyzing the coexpression patterns of both noncoding RNA and mRNA from the same cell, we were able to discover new roles of noncoding RNA throughout essential processes, such as cell cycle and lineage commitment during embryonic development. Moreover, we show that independent classes of short-noncoding RNA can be used to determine cell-type identity.

RR3DD: an RNA global structure-based RNA three-dimensional structural classification database.

The three-dimensional (3D) structure of RNA usually plays an important role in the recognition with RNA-binding protein. Along with the discovering of RNAs, several RNA databases are developed to study the functions of RNA based on sequence, secondary structure, local 3D structural motif and global structure. Based on RNA function and structure, different RNAs are classified and stored in SCOR and DARTS, respectively. The classification of RNA structures is useful in RNA structure prediction and function annotation. However, the SCOR and DARTS are not updated any more. In this study, we present an RNA classification database RR3DD based on RNA fold with the global 3D structural similarity. The RR3DD includes 13,601 RNA chains from PDB and mmCIF format structures which are classified into 780 RNA folds. The RNA chains from PDB and mmCIF format structures are aligned and clustered into 675 and 220 RNA folds, respectively. By analysing the RNA structure in RR3DD, we find that there are 11 clusters with more than 50 members. These clusters include rRNAs, riboswitches, tRNAs and so on. By mapping RR3DD into Rfam, we found that some RNAs without annotation by Rfam can be annotated through structural alignment. For example, we analysed tRNAs and found that tRNA were successfully grouped in RR3DD for which Rfam did not classify them into one family. Finally, we provide a web interface of RR3DD offering functions of browsing RR3DD, annotating RNA 3D structure and finding templates for RNA homology modelling.

The non-coding RNA interactome in joint health and disease.

Non-coding RNAs have distinct regulatory roles in the pathogenesis of joint diseases including osteoarthritis (OA) and rheumatoid arthritis (RA). As the amount of high-throughput profiling studies and mechanistic investigations of microRNAs, long non-coding RNAs and circular RNAs in joint tissues and biofluids has increased, data have emerged that suggest complex interactions among non-coding RNAs that are often overlooked as critical regulators of gene expression. Identifying these non-coding RNAs and their interactions is useful for understanding both joint health and disease. Non-coding RNAs regulate signalling pathways and biological processes that are important for normal joint development but, when dysregulated, can contribute to disease. The specific expression profiles of non-coding RNAs in various disease states support their roles as promising candidate biomarkers, mediators of pathogenic mechanisms and potential therapeutic targets. This Review synthesizes literature published in the past 2 years on the role of non-coding RNAs in OA and RA with a focus on inflammation, cell death, cell proliferation and extracellular matrix dysregulation. Research to date makes it apparent that 'non-coding' does not mean 'non-essential' and that non-coding RNAs are important parts of a complex interactome that underlies OA and RA.

GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions.

Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

Vault RNAs: hidden gems in RNA and protein regulation.

Non-coding RNAs are important regulators of differentiation during embryogenesis as well as key players in the fine-tuning of transcription and furthermore, they control the post-transcriptional regulation of mRNAs under physiological conditions. Deregulated expression of non-coding RNAs is often identified as one major contribution in a number of pathological conditions. Non-coding RNAs are a heterogenous group of RNAs and they represent the majority of nuclear transcripts in eukaryotes. An evolutionary highly conserved sub-group of non-coding RNAs is represented by vault RNAs, named since firstly discovered as component of the largest known ribonucleoprotein complexes called "vault". Although they have been initially described 30 years ago, vault RNAs are largely unknown and their molecular role is still under investigation. In this review we will summarize the known functions of vault RNAs and their involvement in cellular mechanisms.

Identification of protein-protected mRNA fragments and structured excised intron RNAs in human plasma by TGIRT-seq peak calling.

Human plasma contains > 40,000 different coding and non-coding RNAs that are potential biomarkers for human diseases. Here, we used thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) combined with peak calling to simultaneously profile all RNA biotypes in apheresis-prepared human plasma pooled from healthy individuals. Extending previous TGIRT-seq analysis, we found that human plasma contains largely fragmented mRNAs from > 19,000 protein-coding genes, abundant full-length, mature tRNAs and other structured small non-coding RNAs, and less abundant tRNA fragments and mature and pre-miRNAs. Many of the mRNA fragments identified by peak calling correspond to annotated protein-binding sites and/or have stable predicted secondary structures that could afford protection from plasma nucleases. Peak calling also identified novel repeat RNAs, miRNA-sized RNAs, and putatively structured intron RNAs of potential biological, evolutionary, and biomarker significance, including a family of full-length excised intron RNAs, subsets of which correspond to mirtron pre-miRNAs or agotrons.

A unified dinucleotide alphabet describing both RNA and DNA structures.

By analyzing almost 120 000 dinucleotides in over 2000 nonredundant nucleic acid crystal structures, we define 96+1 diNucleotide Conformers, NtCs, which describe the geometry of RNA and DNA dinucleotides. NtC classes are grouped into 15 codes of the structural alphabet CANA (Conformational Alphabet of Nucleic Acids) to simplify symbolic annotation of the prominent structural features of NAs and their intuitive graphical display. The search for nontrivial patterns of NtCs resulted in the identification of several types of RNA loops, some of them observed for the first time. Over 30% of the nearly six million dinucleotides in the PDB cannot be assigned to any NtC class but we demonstrate that up to a half of them can be re-refined with the help of proper refinement targets. A statistical analysis of the preferences of NtCs and CANA codes for the 16 dinucleotide sequences showed that neither the NtC class AA00, which forms the scaffold of RNA structures, nor BB00, the DNA most populated class, are sequence neutral but their distributions are significantly biased. The reported automated assignment of the NtC classes and CANA codes available at dnatco.org provides a powerful tool for unbiased analysis of nucleic acid structures by structural and molecular biologists.

SHARP: hyperfast and accurate processing of single-cell RNA-seq data via ensemble random projection.

To process large-scale single-cell RNA-sequencing (scRNA-seq) data effectively without excessive distortion during dimension reduction, we present SHARP, an ensemble random projection-based algorithm that is scalable to clustering 10 million cells. Comprehensive benchmarking tests on 17 public scRNA-seq data sets show that SHARP outperforms existing methods in terms of speed and accuracy. Particularly, for large-size data sets (more than 40,000 cells), SHARP runs faster than other competitors while maintaining high clustering accuracy and robustness. To the best of our knowledge, SHARP is the only R-based tool that is scalable to clustering scRNA-seq data with 10 million cells.

The landscape of chimeric RNAs in non-diseased tissues and cells.

Chimeric RNAs and their encoded proteins have been traditionally viewed as unique features of neoplasia, and have been used as biomarkers and therapeutic targets for multiple cancers. Recent studies have demonstrated that chimeric RNAs also exist in non-cancerous cells and tissues, although large-scale, genome-wide studies of chimeric RNAs in non-diseased tissues have been scarce. Here, we explored the landscape of chimeric RNAs in 9495 non-diseased human tissue samples of 53 different tissues from the GTEx project. Further, we established means for classifying chimeric RNAs, and observed enrichment for particular classifications as more stringent filters are applied. We experimentally validated a subset of chimeric RNAs from each classification and demonstrated functional relevance of two chimeric RNAs in non-cancerous cells. Importantly, our list of chimeric RNAs in non-diseased tissues overlaps with some entries in several cancer fusion databases, raising concerns for some annotations. The data from this study provides a large repository of chimeric RNAs present in non-diseased tissues, which can be used as a control dataset to facilitate the identification of true cancer-specific chimeras.

Isolation and characterization of microvesicles from mesenchymal stem cells.

Mesenchymal stem or stromal cells are currently under clinical investigation for multiple diseases. While their mechanism of action is still not fully elucidated, vesicles secreted by MSCs are believed to recapitulate their therapeutic potentials to some extent. Microvesicles (MVs), also called as microparticles or ectosome, are among secreted vesicles that could transfer cytoplasmic cargo, including RNA and proteins, from emitting (source) cells to recipient cells. Given the importance of MVs, we here attempted to establish a method to isolate and characterize MVs secreted from unmodified human bone marrow derived MSCs (referred to as native MSCs, and their microvesicles as Native-MVs) and IFNγ stimulated MSCs (referred to as IFNγ-MSCs, and their microvesicles as IFNγ-MVs). We first describe an ultracentrifugation technique to isolate MVs from the conditioned cell culture media of MSCs. Next, we describe characterization and quality control steps to analyze the protein and RNA content of MVs. Finally, we examined the potential of MVs to exert immunomodulatory effects through induction of regulatory T cells (Tregs). Secretory vesicles from MSCs are promising alternatives for cell therapy with applications in drug delivery, regenerative medicine, and immunotherapy.

Integrated analysis of a competing endogenous RNA network in renal cell carcinoma using bioinformatics tools.

BACKGROUND: Circular RNAs (circRNAs) are known to be closely involved in tumorigenesis and cancer progression. Nevertheless, their function and underlying mechanisms in renal cell carcinoma (RCC) remain largely unknown. The aim of the present study was to explore their expression, functions, and molecular mechanisms in RCC. METHODS: We downloaded the circRNA expression profiles from Gene Expression Omnibus (GEO) database, and RNA expression profiles from The Cancer Genome Atlas (TCGA) database. A ceRNA network was constructed based on circRNA-miRNA pairs and miRNA-mRNA pairs. Interactions between proteins were analyzed using the STRING database, and hub genes were identified using the cytoHubba app. We also constructed a circRNA-miRNA-hub gene regulatory module. Functional and pathway enrichment analyses were conducted using "DAVID 6.8" and R package "clusterProfiler". RESULTS: About 6 DEcircRNAs, 17 DEmiRNAs, and 134 DEmRNAs were selected for the construction of ceRNA network of RCC. Protein-protein interaction network and module analysis identified 8 hub genes. A circRNA-miRNA-hub gene sub-network was constructed based on 3 DEcircRNAs, 4 DEmiRNAs, and 8 DEmRNAs. GO and KEGG pathway analysis indicated the possible association of DEmRNAs with RCC onset and progression. CONCLUSIONS: These findings together provide a deeper understanding of the pathogenesis of RCC and suggest potential therapeutic targets.

RNA-Mediated Disease Mechanisms in Neurodegenerative Disorders.

RNA is accurately entangled in virtually all pathways that maintain cellular homeostasis. To name but a few, RNA is the "messenger" between DNA encoded information and the resulting proteins. Furthermore, RNAs regulate diverse processes by forming DNA::RNA or RNA::RNA interactions. Finally, RNA itself can be the scaffold for ribonucleoprotein complexes, for example, ribosomes or cellular bodies. Consequently, disruption of any of these processes can lead to disease. This review describes known and emerging RNA-based disease mechanisms like interference with regular splicing, the anomalous appearance of RNA-protein complexes and uncommon RNA species, as well as non-canonical translation. Due to the complexity and entanglement of the above-mentioned pathways, only few drugs are available that target RNA-based disease mechanisms. However, advances in our understanding how RNA is involved in and modulates cellular homeostasis might pave the way to novel treatments.