3DSNP 2.0: update and expansion of the noncoding genomic variant annotation database.

The rapid development of single-molecule long-read sequencing (LRS) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) technologies presents both challenges and opportunities for the annotation of noncoding variants. Here, we updated 3DSNP, a comprehensive database for human noncoding variant annotation, to expand its applications to structural variation (SV) and to implement variant annotation down to single-cell resolution. The updates of 3DSNP include (i) annotation of 108 317 SVs from a full spectrum of functions, especially their potential effects on three-dimensional chromatin structures, (ii) evaluation of the accessible chromatin peaks flanking the variants across 126 cell types/subtypes in 15 human fetal tissues and 54 cell types/subtypes in 25 human adult tissues by integrating scATAC-seq data and (iii) expansion of Hi-C data to 49 human cell types. In summary, this version is a significant and comprehensive improvement over the previous version. The 3DSNP v2.0 database is freely available at https://omic.tech/3dsnpv2/.

ViRBase v3.0: a virus and host ncRNA-associated interaction repository with increased coverage and annotation.

As a means to aid in the investigation of viral infection mechanisms and identification of more effective antivirus targets, the availability of a source which continually collects and updates information on the virus and host ncRNA-associated interaction resources is essential. Here, we update the ViRBase database to version 3.0 (http://www.virbase.org/ or http://www.rna-society.org/virbase/). This update represents a major revision: (i) the total number of interaction entries is now greater than 820,000, an approximately 70-fold increment, involving 116 virus and 36 host organisms, (ii) it supplements and provides more details on RNA annotations (including RNA editing, RNA localization and RNA modification), ncRNA SNP and ncRNA-drug related information and (iii) it provides two additional tools for predicting binding sites (IntaRNA and PRIdictor), a visual plug-in to display interactions and a website which is optimized for more practical and user-friendly operation. Overall, ViRBase v3.0 provides a more comprehensive resource for virus and host ncRNA-associated interactions enabling researchers a more effective means for investigation of viral infections.

miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions.

MicroRNAs (miRNAs) are noncoding RNAs with 18-26 nucleotides; they pair with target mRNAs to regulate gene expression and produce significant changes in various physiological and pathological processes. In recent years, the interaction between miRNAs and their target genes has become one of the mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA-target interactions (MTIs) verified by biological experiments, miRTarBase has undergone five revisions and enhancements. The database has accumulated >2 200 449 verified MTIs from 13 389 manually curated articles and CLIP-seq data. An optimized scoring system is adopted to enhance this update's critical recognition of MTI-related articles and corresponding disease information. In addition, single-nucleotide polymorphisms and disease-related variants related to the binding efficiency of miRNA and target were characterized in miRNAs and gene 3' untranslated regions. miRNA expression profiles across extracellular vesicles, blood and different tissues, including exosomal miRNAs and tissue-specific miRNAs, were integrated to explore miRNA functions and biomarkers. For the user interface, we have classified attributes, including RNA expression, specific interaction, protein expression and biological function, for various validation experiments related to the role of miRNA. We also used seed sequence information to evaluate the binding sites of miRNA. In summary, these enhancements render miRTarBase as one of the most research-amicable MTI databases that contain comprehensive and experimentally verified annotations. The newly updated version of miRTarBase is now available at https://miRTarBase.cuhk.edu.cn/.

Pol3Base: a resource for decoding the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs.

RNA polymerase III (Pol III) transcribes hundreds of non-coding RNA genes (ncRNAs), which involve in a variety of cellular processes. However, the expression, functions, regulatory networks and evolution of these Pol III-transcribed ncRNAs are still largely unknown. In this study, we developed a novel resource, Pol3Base (http://rna.sysu.edu.cn/pol3base/), to decode the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs. The current release of Pol3Base includes thousands of regulatory relationships between ∼79 000 ncRNAs and transcription factors by mining 56 ChIP-seq datasets. By integrating CLIP-seq datasets, we deciphered the interactions of these ncRNAs with >240 RNA binding proteins. Moreover, Pol3Base contains ∼9700 RNA modifications located within thousands of Pol III-transcribed ncRNAs. Importantly, we characterized expression profiles of ncRNAs in >70 tissues and 28 different tumor types. In addition, by comparing these ncRNAs from human and mouse, we revealed about 4000 evolutionary conserved ncRNAs. We also identified ∼11 403 tRNA-derived small RNAs (tsRNAs) in 32 different tumor types. Finally, by analyzing somatic mutation data, we investigated the mutation map of these ncRNAs to help uncover their potential roles in diverse diseases. This resource will help expand our understanding of potential functions and regulatory networks of Pol III-transcribed ncRNAs.

RNALocate v2.0: an updated resource for RNA subcellular localization with increased coverage and annotation.

Resolving the spatial distribution of the transcriptome at a subcellular level can increase our understanding of biology and diseases. To facilitate studies of biological functions and molecular mechanisms in the transcriptome, we updated RNALocate, a resource for RNA subcellular localization analysis that is freely accessible at http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/. Compared to RNALocate v1.0, the new features in version 2.0 include (i) expansion of the data sources and the coverage of species; (ii) incorporation and integration of RNA-seq datasets containing information about subcellular localization; (iii) addition and reorganization of RNA information (RNA subcellular localization conditions and descriptive figures for method, RNA homology information, RNA interaction and ncRNA disease information) and (iv) three additional prediction tools: DM3Loc, iLoc-lncRNA and iLoc-mRNA. Overall, RNALocate v2.0 provides a comprehensive RNA subcellular localization resource for researchers to deconvolute the highly complex architecture of the cell.

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.

Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.

Apart from protein-coding Ribonucleic acids (RNAs), there exists a variety of non-coding RNAs (ncRNAs) which regulate complex cellular and molecular processes. High-throughput sequencing technologies and bioinformatics approaches have largely promoted the exploration of ncRNAs which revealed their crucial roles in gene regulation, miRNA binding, protein interactions, and splicing. Furthermore, ncRNAs are involved in the development of complicated diseases like cancer. Categorization of ncRNAs is essential to understand the mechanisms of diseases and to develop effective treatments. Sub-cellular localization information of ncRNAs demystifies diverse functionalities of ncRNAs. To date, several computational methodologies have been proposed to precisely identify the class as well as sub-cellular localization patterns of RNAs). This paper discusses different types of ncRNAs, reviews computational approaches proposed in the last 10 years to distinguish coding-RNA from ncRNA, to identify sub-types of ncRNAs such as piwi-associated RNA, micro RNA, long ncRNA, and circular RNA, and to determine sub-cellular localization of distinct ncRNAs and RNAs. Furthermore, it summarizes diverse ncRNA classification and sub-cellular localization determination datasets along with benchmark performance to aid the development and evaluation of novel computational methodologies. It identifies research gaps, heterogeneity, and challenges in the development of computational approaches for RNA sequence analysis. We consider that our expert analysis will assist Artificial Intelligence researchers with knowing state-of-the-art performance, model selection for various tasks on one platform, dominantly used sequence descriptors, neural architectures, and interpreting inter-species and intra-species performance deviation.

Mitochondrial noncoding RNAs: new wine in an old bottle.

Mitochondrial noncoding RNAs (mt-ncRNAs) include noncoding RNAs inside the mitochondria that are transcribed from the mitochondrial genome or nuclear genome, and noncoding RNAs transcribed from the mitochondrial genome that are transported to the cytosol or nucleus. Recent findings have revealed that mt-ncRNAs play important roles in not only mitochondrial functions, but also other cellular activities. This review proposes a classification of mt-ncRNAs and outlines the emerging understanding of mitochondrial circular RNAs (mt-circRNAs), mitochondrial microRNAs (mitomiRs), and mitochondrial long noncoding RNAs (mt-lncRNAs), with an emphasis on their identification and functions.

Tetraarsenic oxide affects non-coding RNA transcriptome through deregulating polycomb complexes in MCF7 cells.

Non-coding RNAs (ncRNAs) play important and diverse roles in mammalian cell biology and pathology. Although the functions of an increasing number of ncRNAs have been identified, the mechanisms underlying ncRNA gene expression remain elusive and are incompletely understood. Here, we investigated ncRNA gene expression in Michigan cancer foundation 7 (MCF7), a malignant breast cancer cell line, on treatment of tetraarsenic oxide (TAO), a potential anti-cancer drug. Our genomic analyses found that TAO up- or down-regulated ncRNA genes genome-wide. A subset of identified ncRNAs with critical biological and clinical functions were validated by real-time quantitative polymerase chain reaction. Intriguingly, these TAO-regulated genes included CDKN2B-AS, HOXA11-AS, SHH, and DUSP5 that are known to interact with or be targeted by polycomb repressive complexes (PRCs). In addition, the PRC subunits were enriched in these TAO-regulated ncRNA genes and TAO treatment deregulated the expression of PRC subunits. Strikingly, TAO decreased the cellular and gene-specific levels of EZH2 expression and H3K27me3. In particular, TAO reduced EZH2 and H3K27me3 and increased transcription at MALAT1 gene. Inhibiting the catalytic activity of EZH2 using GSK343 increased representative TAO-inducible ncRNA genes. Together, our findings suggest that the expression of a subset of ncRNA genes is regulated by PRC2 and that TAO could be a potent epigenetic regulator through PRCs to modulate the ncRNA gene expression in MCF7 cells.

Regulatory Non-Coding RNAs: An Overview.

The discovery of new classes of non-coding RNAs has always been preceded or accompanied by technological breakthroughs, and these outstanding progresses in transcriptomics approaches enabled to regularly add new members to the list. From the first detection of tRNAs, through the revolution of miRNAs discovery, to the recent identification of eRNAs or the identification of new functions for already known ncRNAs, this introductive review provides a very concise historical and functional overview of most prominent small regulatory non-coding RNA families.

Virus-associated ribozymes and nano carriers against COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a zoo tonic, highly pathogenic virus. The new type of coronavirus with contagious nature spread from Wuhan (China) to the whole world in a very short time and caused the new coronavirus disease (COVID-19). COVID-19 has turned into a global public health crisis due to spreading by close person-to-person contact with high transmission capacity. Thus, research about the treatment of the damages caused by the virus or prevention from infection increases everyday. Besides, there is still no approved and definitive, standardized treatment for COVID-19. However, this disaster experienced by human beings has made us realize the significance of having a system ready for use to prevent humanity from viral attacks without wasting time. As is known, nanocarriers can be targeted to the desired cells in vitro and in vivo. The nano-carrier system targeting a specific protein, containing the enzyme inhibiting the action of the virus can be developed. The system can be used by simple modifications when we encounter another virus epidemic in the future. In this review, we present a potential treatment method consisting of a nanoparticle-ribozyme conjugate, targeting ACE-2 receptors by reviewing the virus-associated ribozymes, their structures, types and working mechanisms.

MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation.

Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.

ncRFP: A Novel end-to-end Method for Non-Coding RNAs Family Prediction Based on Deep Learning.

Evidence has accumulated enough to prove non-coding RNAs (ncRNAs) play important roles in cellular biological processes and disease pathogenesis. High throughput techniques have produced a large number of ncRNAs whose function remains unknown. Since the accurate identification of ncRNAs family is helpful to the research of their function, it is of necessity and urgency to predict the family of each ncRNAs. Although several traditional excellent methods are applicable to predict the family of ncRNAs, their complex procedures or inaccurate performance remain major problems confronting us. The main idea of those methods is first to predict the secondary structure, and then identify ncRNAs family according to properties of the secondary structure. Unfortunately, the multi-step error superposition, especially the imperfection of RNA secondary structure prediction tools, maybe the cause of low accuracy. In this paper, a novel end-to-end method 'ncRFP' was proposed to complete the prediction task based on Deep Learning. Instead of predicting the secondary structure, ncRFP predicts the ncRNAs family by automatically extracting features from ncRNAs sequences. Compared with other methods, ncRFP not only simplifies the process but also improves accuracy. The source code of ncRFP can be available at https://github.com/linyuwangPHD/ncRFP.

CNCDatabase: a database of non-coding cancer drivers.

Most mutations in cancer genomes occur in the non-coding regions with unknown impact on tumor development. Although the increase in the number of cancer whole-genome sequences has revealed numerous putative non-coding cancer drivers, their information is dispersed across multiple studies making it difficult to understand their roles in tumorigenesis of different cancer types. We have developed CNCDatabase, Cornell Non-coding Cancer driver Database (https://cncdatabase.med.cornell.edu/) that contains detailed information about predicted non-coding drivers at gene promoters, 5' and 3' UTRs (untranslated regions), enhancers, CTCF insulators and non-coding RNAs. CNCDatabase documents 1111 protein-coding genes and 90 non-coding RNAs with reported drivers in their non-coding regions from 32 cancer types by computational predictions of positive selection using whole-genome sequences; differential gene expression in samples with and without mutations; or another set of experimental validations including luciferase reporter assays and genome editing. The database can be easily modified and scaled as lists of non-coding drivers are revised in the community with larger whole-genome sequencing studies, CRISPR screens and further experimental validations. Overall, CNCDatabase provides a helpful resource for researchers to explore the pathological role of non-coding alterations in human cancers.

RNAcentral 2021: secondary structure integration, improved sequence search and new member databases.

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world's largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org.

Rfam 14: expanded coverage of metagenomic, viral and microRNA families.

Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

Single-cell RNA profiling links ncRNAs to spatiotemporal gene expression during C. elegans embryogenesis.

Recent studies show that non-coding RNAs (ncRNAs) can regulate the expression of protein-coding genes and play important roles in mammalian development. Previous studies have revealed that during C. elegans (Caenorhabditis elegans) embryo development, numerous genes in each cell are spatiotemporally regulated, causing the cell to differentiate into distinct cell types and tissues. We ask whether ncRNAs participate in the spatiotemporal regulation of genes in different types of cells and tissues during the embryogenesis of C. elegans. Here, by using marker-free full-length high-depth single-cell RNA sequencing (scRNA-seq) technique, we sequence the whole transcriptomes from 1031 embryonic cells of C. elegans and detect 20,431 protein-coding genes, including 22 cell-type-specific protein-coding markers, and 9843 ncRNAs including 11 cell-type-specific ncRNA markers. We induce a ncRNAs-based clustering strategy as a complementary strategy to the protein-coding gene-based clustering strategy for single-cell classification. We identify 94 ncRNAs that have never been reported to regulate gene expressions, are co-expressed with 1208 protein-coding genes in cell type specific and/or embryo time specific manners. Our findings suggest that these ncRNAs could potentially influence the spatiotemporal expression of the corresponding genes during the embryogenesis of C. elegans.

Low-light and its effects on crop yield: Genetic and genomic implications.

Shade indicates decreased sunlight. The agricultural importance of shade imparts to its deteriorative effect of crop yield. Rice is not only the most widely used food crop by a third of the population of the world, but it has also been established as the modelmonocot plant for study. This article describes several important aspects of shade on rice yield with appropriate examples in other plants such as Arabidopsis. To start with, how different environmental or growth conditions create shade is explained. Themorphological, physiological and biochemical characteristics due to different kinds of shade are selectively explained. The molecular characteristics of rice under shade from genetic, genomic and epigenetic studied are discussed. Signalling components for the manifestation of shade tolerance responses and their interconnection with other signalling networks and hormone pathway components are from recent reports. A list of genes, micro-RNAs and metabolites that are involved in shade responses is presented. Lastly, implications for sustainable yield under shade is discussed. This review will be useful not only for cutting-edge information on shade tolerance but will also build framework for upcoming new rice varieties with sustainable yield under shade.

Comparative analysis of the down syndrome hippocampal non-coding RNA transcriptomes using a mouse model.

BACKGROUND: Down syndrome (DS), caused by trisomy 21, is the most common human chromosomal disorder. Hippocampal abnormalities have been believed to be responsible for the DS developmental cognitive deficits. Cumulative evidences indicated that non-coding RNAs (ncRNAs) participated in brain development and function. Currently, few was known whether dysregulated ncRNAs existed in DS whether the dysregulated ncRNAs played important pathology roles in DS. OBJECTIVE: The purpose of this study was generating an overview map of the dysregulated ncRNAs in DS, including the microRNA (miRNA), long ncRNA (lncRNA) and circular RNA (circRNAs). DS mouse models are invaluable tools for further mechanism and therapy studies. METHODS: The well-studied DS mouse model Dp(16)1/Yey was used in this study as it contains the trisomy of the whole human chromosome 21 syntenic region on mouse chromosomes 16. Hippocampi were isolated from pups of seven-days-old. Libraries for miRNA, lncRNA and circRNAs were constructed separately, and the next generation sequencing method was utilized. RESULTS: Differentially expressed (DE) miRNAs, lncRNAs and circRNAs were reported. Relative few regulating relationship were found between the DE miRNAs and DE mRNAs. LncRNAs originated from the trisomic regions expressed in clusters, but not all of them were 1.5-fold increased expressed. Dramatic DE circular RNAs were found in the DS hippocampus. The host genes of the DE circRNAs were enriched on functions which were well-known impaired in DS, e.g. long-term-potentiation, glutamatergic synapse, and GABAergic synapse. CONCLUSIONS: We generated the first DS developmental hippocampal ncRNA transcriptome map. This work laid foundations for further investigations on role of ncRNAs in hippocampal functions.

ncRDeep: Non-coding RNA classification with convolutional neural network.

A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involved in many biological processes, diseases, and cancers. Numerous ncRNAs have been identified and classified with high throughput sequencing technology. Hence, accurate ncRNAs class prediction is important and necessary for further study of their functions. Several computation techniques have been employed to predict the class of ncRNAs. Recent classification methods used the secondary structure as their primary input. However, the computational tools of RNA secondary structure are not accurate enough which affects the final performance of ncRNAs predictors. In this paper, we propose a simple yet efficient method, called ncRDeep, for ncRNAs prediction. It uses a simple convolutional neural network and RNA sequence information only. The ncRDeep was evaluated on benchmark datasets and the comparison results showed that the ncRDeep outperforms the state-of-the-art methods significantly. More specifically, the average accuracy was improved by 8.32%. Finally, we built a freely accessible web server for the developed tool ncRDeep at http://home.jbnu.ac.kr/NSCL/ncRDeep.htm.