CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers.

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA-target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA-miRNA-gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

CSCD2: an integrated interactional database of cancer-specific circular RNAs.

The significant function of circRNAs in cancer was recognized in recent work, so a well-organized resource is required for characterizing the interactions between circRNAs and other functional molecules (such as microRNA and RNA-binding protein) in cancer. We previously developed cancer-specific circRNA database (CSCD), a comprehensive database for cancer-specific circRNAs, which is widely used in circRNA research. Here, we updated CSCD to CSCD2 (http://geneyun.net/CSCD2 or http://gb.whu.edu.cn/CSCD2), which includes significantly more cancer-specific circRNAs identified from a large number of human cancer and normal tissues/cell lines. CSCD2 contains >1000 samples (825 tissues and 288 cell lines) and identifies a large number of circRNAs: 1 013 461 cancer-specific circRNAs, 1 533 704 circRNAs from only normal samples and 354 422 circRNAs from both cancer and normal samples. In addition, CSCD2 predicts potential miRNA-circRNA and RBP-circRNA interactions using binding motifs from >200 RBPs and 2000 microRNAs. Furthermore, the potential full-length and open reading frame sequence of these circRNAs were also predicted. Collectively, CSCD2 provides a significantly enhanced resource for exploring the function and regulation of circRNAs in cancer.

circMine: a comprehensive database to integrate, analyze and visualize human disease-related circRNA transcriptome.

Many circRNA transcriptome data were deposited in public resources, but these data show great heterogeneity. Researchers without bioinformatics skills have difficulty in investigating these invaluable data or their own data. Here, we specifically designed circMine (http://hpcc.siat.ac.cn/circmine and http://www.biomedical-web.com/circmine/) that provides 1 821 448 entries formed by 136 871 circRNAs, 87 diseases and 120 circRNA transcriptome datasets of 1107 samples across 31 human body sites. circMine further provides 13 online analytical functions to comprehensively investigate these datasets to evaluate the clinical and biological significance of circRNA. To improve the data applicability, each dataset was standardized and annotated with relevant clinical information. All of the 13 analytic functions allow users to group samples based on their clinical data and assign different parameters for different analyses, and enable them to perform these analyses using their own circRNA transcriptomes. Moreover, three additional tools were developed in circMine to systematically discover the circRNA-miRNA interaction and circRNA translatability. For example, we systematically discovered five potential translatable circRNAs associated with prostate cancer progression using circMine. In summary, circMine provides user-friendly web interfaces to browse, search, analyze and download data freely, and submit new data for further integration, and it can be an important resource to discover significant circRNA in different diseases.

POSTAR3: an updated platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation. Accurate identification of RBP binding sites in multiple cell lines and tissue types from diverse species is a fundamental endeavor towards understanding the regulatory mechanisms of RBPs under both physiological and pathological conditions. Our POSTAR annotation processes make use of publicly available large-scale CLIP-seq datasets and external functional genomic annotations to generate a comprehensive map of RBP binding sites and their association with other regulatory events as well as functional variants. Here, we present POSTAR3, an updated database with improvements in data collection, annotation infrastructure, and analysis that support the annotation of post-transcriptional regulation in multiple species including: we made a comprehensive update on the CLIP-seq and Ribo-seq datasets which cover more biological conditions, technologies, and species; we added RNA secondary structure profiling for RBP binding sites; we provided miRNA-mediated degradation events validated by degradome-seq; we included RBP binding sites at circRNA junction regions; we expanded the annotation of RBP binding sites, particularly using updated genomic variants and mutations associated with diseases. POSTAR3 is freely available at http://postar.ncrnalab.org.

ViroidDB: a database of viroids and viroid-like circular RNAs.

We introduce ViroidDB, a value-added database that attempts to collect all known viroid and viroid-like circular RNA sequences into a single resource. Spanning about 10 000 unique sequences, ViroidDB includes viroids, retroviroid-like elements, small circular satellite RNAs, ribozyviruses, and retrozymes. Each sequence's secondary structure, ribozyme content, and cluster membership are predicted via a custom pipeline optimized for handling circular RNAs. The data can be explored via a purpose-built user interface that features visualizations, multiple sequence alignments, and a portal for downloading bulk data. Users can browse the data by sequence type, taxon, or typo-tolerant search of metadata fields. The database is freely accessible at https://viroids.org.

exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids.

Extracellular vesicles (EVs) are small membranous vesicles that contain an abundant cargo of different RNA species with specialized functions and clinical implications. Here, we introduce an updated online database (http://www.exoRBase.org), exoRBase 2.0, which is a repository of EV long RNAs (termed exLRs) derived from RNA-seq data analyses of diverse human body fluids. In exoRBase 2.0, the number of exLRs has increased to 19 643 messenger RNAs (mRNAs), 15 645 long non-coding RNAs (lncRNAs) and 79 084 circular RNAs (circRNAs) obtained from ∼1000 human blood, urine, cerebrospinal fluid (CSF) and bile samples. Importantly, exoRBase 2.0 not only integrates and compares exLR expression profiles but also visualizes the pathway-level functional changes and the heterogeneity of origins of circulating EVs in the context of different physiological and pathological conditions. Our database provides an attractive platform for the identification of novel exLR signatures from human biofluids that will aid in the discovery of new circulating biomarkers to improve disease diagnosis and therapy.

Attention-Based Deep Multiple-Instance Learning for Classifying Circular RNA and Other Long Non-Coding RNA.

Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture fed with a raw sequence, to learn the sparse features of RNA sequences and to accomplish the circRNAs identification task. The model outperformed the state-of-art models. Moreover, following the validation of the attention mechanism effectiveness by the handwritten digit dataset, the key sequence loci underlying circRNA's recognition were obtained based on the corresponding attention score. Then, motif enrichment analysis identified some of the key motifs for circRNA formation. In conclusion, we designed deep learning network architecture suitable for learning gene sequences with sparse features and implemented it for the circRNA identification task, and the model has strong representation capability in the indication of some key loci.

Non-Coding RNAs in Legumes: Their Emerging Roles in Regulating Biotic/Abiotic Stress Responses and Plant Growth and Development.

Noncoding RNAs, including microRNAs (miRNAs), small interference RNAs (siRNAs), circular RNA (circRNA), and long noncoding RNAs (lncRNAs), control gene expression at the transcription, post-transcription, and translation levels. Apart from protein-coding genes, accumulating evidence supports ncRNAs playing a critical role in shaping plant growth and development and biotic and abiotic stress responses in various species, including legume crops. Noncoding RNAs (ncRNAs) interact with DNA, RNA, and proteins, modulating their target genes. However, the regulatory mechanisms controlling these cellular processes are not well understood. Here, we discuss the features of various ncRNAs, including their emerging role in contributing to biotic/abiotic stress response and plant growth and development, in addition to the molecular mechanisms involved, focusing on legume crops. Unravelling the underlying molecular mechanisms and functional implications of ncRNAs will enhance our understanding of the coordinated regulation of plant defences against various biotic and abiotic stresses and for key growth and development processes to better design various legume crops for global food security.

CircRNA expression profiling and bioinformatics analysis indicate the potential biological role and clinical significance of circRNA in influenza A virus-induced lung injury.

Circular RNA (circRNA) plays an important role in the regulation of multiple biological processes. However, circRNA profiling and the potential biological role of circRNA in influenza A virus (IAV)-induced lung injury have not been investigated. In the present study, circRNA expression profiles in lung tissues from mice with and without IAV-induced lung injury were analyzed using high-throughput sequencing, and differentially expressed circRNAs were verified by quantitative PCR. The gene homology of candidate circRNAs was investigated and the expression of plasma circRNAs from patients with IAV-induced acute respiratory distress syndrome (ARDS) was detected. The target microRNAs (miRNAs) of circRNAs were predicted. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. In total, 781 circRNAs were differentially expressed between ARDS mice and control (467 were up-regulated and 314 were down-regulated). Moreover, the candidate circRNAs (Slco3a1, Nfatc2, Wdr33, and Dmd) expression showed the same trend with the sequencing results. The isoforms of circRNA Slco3a1 and Wdr33 were highly conserved between humans and mice. Plasma circRNA Slco3a1 and Wdr33 presented differential expression in patients with IAV-induced ARDS compared to control. The circRNAmiRNA interaction network and GO and KEGG analyses indicated the potential biological role of circRNAs in the development of IAV-induced lung injury. Taken together, a large number of differentially expressed circRNAs were identified in our study. CircRNA Slco3a1 and Wdr33 had significantly different expression in specimens from mice and humans, and showed a potential biological role in IAV-induced lung injury by bioinformatics analysis.

CircAMD1 regulates proliferation and collagen synthesis via sponging miR-27a-3p in P63-mutant human dermal fibroblasts.

Transcription factor p63 has critical functions in epidermal, hindgut/anorectal, and limb development. Human mutations in P63 correlate with congenital syndromes affecting the skin, anorectal, and limbs. Nevertheless, less are detected regarding networks and functions controlled by P63 mutations in dermal fibroblasts, which are closely related to skin physiology. To screen for new targets, we employed microarray technology to investigate the R226Q P63 mutation with regards to the resulting circular RNA (circRNA) profiles from P63 point mutations in human dermal fibroblasts (HDFs). In this study, we show that P63-mutant HDFs display reduced proliferation, collagen synthesis, and myofibroblast differentiation; circAMD1 was also downregulated in P63-mutant HDFs compared with wild-type HDFs. Furthermore, overexpressing circAMD1 rescued the functional and phenotypic alterations of p63-mutant HDFs. We as well determined that miR-27a-3p was circAMD1 target involved in effects of circAMD1 in P63-mutant HDFs. Collectively, our data show that circAMD1 functions as a miR-27a-3p sponge that inhibits the functional and phenotypical alteration of P63-mutant HDFs and may be a critical marker in pathogenesis regarding P63-associated traits.

SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.

Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biological data to identify the association between circRNA and disease can effectively reduce cost and save time. Considering the limitations of existing computational models, we propose a semi-supervised generative adversarial network (GAN) model SGANRDA for predicting circRNA-disease association. This model first fused the natural language features of the circRNA sequence and the features of disease semantics, circRNA and disease Gaussian interaction profile kernel, and then used all circRNA-disease pairs to pre-train the GAN network, and fine-tune the network parameters through labeled samples. Finally, the extreme learning machine classifier is employed to obtain the prediction result. Compared with the previous supervision model, SGANRDA innovatively introduced circRNA sequences and utilized all the information of circRNA-disease pairs during the pre-training process. This step can increase the information content of the feature to some extent and reduce the impact of too few known associations on the model performance. SGANRDA obtained AUC scores of 0.9411 and 0.9223 in leave-one-out cross-validation and 5-fold cross-validation, respectively. Prediction results on the benchmark dataset show that SGANRDA outperforms other existing models. In addition, 25 of the top 30 circRNA-disease pairs with the highest scores of SGANRDA in case studies were verified by recent literature. These experimental results demonstrate that SGANRDA is a useful model to predict the circRNA-disease association and can provide reliable candidates for biological experiments.

Role of circular RNAs in colorectal tumor microenvironment.

Circular RNAs (circRNAs) are a class of endogenous noncoding RNA, which were previously considered as a byproduct of RNA splicing error. Numerous studies have demonstrated the altered expression of circRNAs in organ tissues during pathological conditions and their involvements in disease pathogenesis and progression, including cancers. In colorectal cancer (CRC), multiple circRNAs have been identified and characterized as "oncogenic", given their involvements in the downregulation of tumor suppressor genes and induction of tumor initiation, progression, invasion, and metastasis. Additionally, other circRNAs have been identified in CRC and characterized as "tumor suppressive" based on their ability of inhibiting the expression of oncogenic genes and suppressing tumor growth and proliferation. circRNAs could serve as potential diagnostic and prognostic biomarkers, and therapeutic targets or vectors to be utilized in cancer therapies. This review briefly describes the dynamic changes of the tumor microenvironment inducing immunosuppression and tumorigenesis, and outlines the biogenesis and characteristics of circRNAs and recent findings indicating their roles and functions in the CRC tumor microenvironment. It also discusses strategies and technologies, which could be employed in the future to overcome current cancer therapy challenges associated with circRNAs.

Feature extraction approaches for biological sequences: a comparative study of mathematical features.

As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification. Availability:https://github.com/Bonidia/FeatureExtraction_BiologicalSequences.

Differentially Expressed Circular RNA Profile in an Intracranial Aneurysm Group Compared with a Healthy Control Group.

OBJECTIVE: Intracranial aneurysm (IA) is a fatal disease owing to vascular rupture and subarachnoid hemorrhage. Much attention has been given to circular RNAs (circRNAs) because they may be potential biomarkers for many diseases, but their mechanism in the formation of IA remains unknown. METHODS: circRNA expression profile analysis of blood samples was conducted between patients with IA and controls. Overall, 235 differentially expressed circRNAs were confirmed between IA patients and the control group. The reliability of the microarray results was demonstrated by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: Of 235 differentially expressed genes, 150 were upregulated, while the other 85 were downregulated. Five miRNAs matched to every differential expression of circRNAs, and related MREs were predicted. We performed gene ontology (GO) analysis to identify the functions of their targeted genes, with the terms "Homophilic cell adhesion via plasma membrane adhesion molecules" and "Positive regulation of cellular process" showing the highest fold enrichment. CONCLUSIONS: This study demonstrated the role of circRNA expression profiling in the formation of IA and revealed that the mTOR pathway can be a latent therapeutic strategy for IA.

LincSNP 3.0: an updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements.

We describe an updated comprehensive database, LincSNP 3.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), which aims to document and annotate disease or phenotype-associated variants in human long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) or their regulatory elements. LincSNP 3.0 has updated with several novel features, including (i) more types of variants including single nucleotide polymorphisms (SNPs), linkage disequilibrium SNPs (LD SNPs), somatic mutations and RNA editing sites have been expanded; (ii) more regulatory elements including transcription factor binding sites (TFBSs), enhancers, DNase I hypersensitive sites (DHSs), topologically associated domains (TADs), footprintss, methylations and open chromatin regions have been added; (iii) the associations among circRNAs, regulatory elements and variants have been identified; (iv) more experimentally supported variant-lncRNA/circRNA-disease/phenotype associations have been manually collected; (v) the sources of lncRNAs, circRNAs, SNPs, somatic mutations and RNA editing sites have been updated. Moreover, four flexible online tools including Genome Browser, Variant Mapper, Circos Plotter and Functional Annotation have been developed to retrieve, visualize and analyze the data. Collectively, LincSNP 3.0 provides associations among functional variants, regulatory elements, lncRNAs and circRNAs in diseases. It will serve as an important and continually updated resource for investigating functions and mechanisms of lncRNAs and circRNAs in diseases.

Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data.

An updated Lnc2Cancer 3.0 (http://www.bio-bigdata.net/lnc2cancer or http://bio-bigdata.hrbmu.edu.cn/lnc2cancer) database, which includes comprehensive data on experimentally supported long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) associated with human cancers. In addition, web tools for analyzing lncRNA expression by high-throughput RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) are described. Lnc2Cancer 3.0 was updated with several new features, including (i) Increased cancer-associated lncRNA entries over the previous version. The current release includes 9254 lncRNA-cancer associations, with 2659 lncRNAs and 216 cancer subtypes. (ii) Newly adding 1049 experimentally supported circRNA-cancer associations, with 743 circRNAs and 70 cancer subtypes. (iii) Experimentally supported regulatory mechanisms of cancer-related lncRNAs and circRNAs, involving microRNAs, transcription factors (TF), genetic variants, methylation and enhancers were included. (iv) Appending experimentally supported biological functions of cancer-related lncRNAs and circRNAs including cell growth, apoptosis, autophagy, epithelial mesenchymal transformation (EMT), immunity and coding ability. (v) Experimentally supported clinical relevance of cancer-related lncRNAs and circRNAs in metastasis, recurrence, circulation, drug resistance, and prognosis was included. Additionally, two flexible online tools, including RNA-seq and scRNA-seq web tools, were developed to enable fast and customizable analysis and visualization of lncRNAs in cancers. Lnc2Cancer 3.0 is a valuable resource for elucidating the associations between lncRNA, circRNA and cancer.

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.

EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments.

Long non-coding RNAs (lncRNAs) play important functional roles in many diverse biological processes. However, not all expressed lncRNAs are functional. Thus, it is necessary to manually collect all experimentally validated functional lncRNAs (EVlncRNA) with their sequences, structures, and functions annotated in a central database. The first release of such a database (EVLncRNAs) was made using the literature prior to 1 May 2016. Since then (till 15 May 2020), 19 245 articles related to lncRNAs have been published. In EVLncRNAs 2.0, these articles were manually examined for a major expansion of the data collected. Specifically, the number of annotated EVlncRNAs, associated diseases, lncRNA-disease associations, and interaction records were increased by 260%, 320%, 484% and 537%, respectively. Moreover, the database has added several new categories: 8 lncRNA structures, 33 exosomal lncRNAs, 188 circular RNAs, and 1079 drug-resistant, chemoresistant, and stress-resistant lncRNAs. All records have checked against known retraction and fake articles. This release also comes with a highly interactive visual interaction network that facilitates users to track the underlying relations among lncRNAs, miRNAs, proteins, genes and other functional elements. Furthermore, it provides links to four new bioinformatics tools with improved data browsing and searching functionality. EVLncRNAs 2.0 is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs2/.

cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function.

RNA endowed with both protein-coding and noncoding functions is referred to as 'dual-function RNA', 'binary functional RNA (bifunctional RNA)' or 'cncRNA (coding and noncoding RNA)'. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.

Circular RNAs as important players in human gastric cancer.

As one of the most prevalent gastrointestinal diseases, gastric cancer (GC) is the second leading cause of cancer-related deaths worldwide. Since GC has no clinical manifestations in the early stage of the disease, most patients are detected in the later phases of disease and have an unfortunately lower chance of recovery. Circular RNAs (circRNAs), a novel category of non-coding RNAs (ncRNAs), are mainly engaged in the regulation of gene expression at the transcriptional and post-transcriptional levels. Numerous evidences have revealed that circRNAs play key roles in GC as they are involved in cell proliferation, growth, and apoptosis via modulating the expression of some target genes, miRNAs, and proteins. Many studies have addressed the impact of circRNA dysregulation on GC initiation, progression, and invasion via binding to miRNAs or RNA binding proteins. Moreover, changes in circRNA expression are associated with pathological and clinical features of GC highlighting their potentials as diagnostic or prognostic biomarkers in GC. In the current study, the recent findings on the significance of circRNAs in the development and progression of GC are reviewed. We focus on the implications of circRNAs as potential diagnostic or prognostic biomarkers in this malignancy.