CircFunBase: a database for functional circular RNAs

GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations

Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA-disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA-disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA-disease associations.

CircRNA circFOXK2 facilitates oncogenesis in breast cancer via IGF2BP3/miR-370 axis

Metastasis is the leading cause of breast cancer (BC)-related deaths. Circular RNAs (circRNAs) have emerged as essential regulators for cancer progression and metastasis. Therefore, the objective of this study was to investigate the role of circRNAs in BC metastasis and related mechanism. In this study, we established the BC cell line with high or low potential of metastasis. RNA sequencing, migration and invasion assay, Fluorescence in situ hybridization, luciferase report assay, circRNA pulldown, and transmission electron microscopy were performed to elucidate the molecular mechanism. The results showed that circRNA circFOXK2 was significantly increased in BC cells with high metastatic ability, and the upregulation of circFOXK2 was correlated with poor clinicopathological characteristics. Functional experiments demonstrated that overexpression of circFOXK2 promoted migration and invasion of BC cells. Also. circFOXK2 could act with IGF2BP3, an RNA-binding protein, and miR-370 to synergistically promote BC metastasis. Moreover, miR-370 could be transferred through exosomes to enhance the metastatic ability of recipient cells. In conclusion, circFOXK2 functions as a key regulator in BC metastasis, and the role of circFOXK2 on BC metastasis is tightly associated with the involvement of IGF2BP3 and miR-370. CircFOXK2 might serve as a potential biomarker for the diagnosis and treatment of BC.

MSCFS: inferring circRNA functional similarity based on multiple data sources

Background

More and more evidence shows that circRNA plays an important role in various biological processes and human health. Therefore, inferring the circRNA’s potential functions and obtaining circRNA functional similarity has become more and more significant. However, there is no effective approach to explore the functional similarity of circRNAs.

Methods

In this paper, we propose a new approach, called MSCFS, to calculate the functional similarity of circRNA by integrating multiple data sources. We combine circRNA-disease association, circRNA-gene-Gene Ontology association, and circRNA sequence information to explore the functional similarity of circRNA. Firstly, we employ different learning representation methods from three data sources to establish three circRNA functional similarity networks. Then we integrate the three networks to obtain the final circRNA functional similarity.

Results

We utilize circRNA–miRNA association similarity and circRNA co-expression similarity to evaluate the performance of MSCFS. The results show a positive correlation with miRNA association (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=0.213$$\end{document}R=0.213) and circRNA co-expression similarity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R=0.8991$$\end{document}R=0.8991). Finally, we construct a circRNA functional similarity network and perform case analysis. The result shows our method can be applied to infer new potential functions of circRNA and other associations.

Conclusions

MSCFS combines multiple data sources related to circRNA functions. Correlation analysis and case analyses prove that MSCFS is a useful method to explore circRNA functional similarity.

NGS Methodologies and Computational Algorithms for the Prediction and Analysis of Plant Circular RNAs

Circular RNAs (circRNAs) are a class of single-stranded RNAs derived from exonic, intronic, and intergenic regions from precursor messenger RNAs (pre-mRNA), where a noncanonical back-splicing event occurs, in which the 5' and 3' ends are attached by covalent bond. CircRNAs participate in the regulation of gene expression at the transcriptional and posttranscriptional level primarily as miRNA and RNA-binding protein (RBP) sponges, but also involved in the regulation of alternative RNA splicing and transcription. CircRNAs are widespread and abundant in plants where they have been involved in stress responses and development. Through the analysis of all publications in this field in the last five years, we can summarize that the identification of these molecules is carried out through next generation sequencing studies, where samples have been previously treated to eliminate DNA, rRNA, and linear RNAs as a means to enrich circRNAs. Once libraries are prepared, they are sequenced and subsequently studied from a bioinformatics point of view. Among the different tools for identifying circRNAs, we can highlight CIRI as the most used (in 60% of the published studies), as well as CIRCExplorer (20%) and find_circ (20%). Although it is recommended to use more than one program in combination, and preferably developed specifically to treat with plant samples, this is not always the case. It should also be noted that after identifying these circular RNAs, most of the authors validate their findings in the laboratory in order to obtain bona fide results.

Circular RNA Databases

Circular RNAs (circRNAs) are a class of endogenous ncRNAs with covalently closed-loop structures, lacking of 5' caps and 3' tails. These novel ncRNAs are ubiquitously expressing in eukaryotes, exhibiting expression patterns of specific cell types, tissues, or developmental stages. CircRNAs have been reported to play important roles in various biological processes, such as regulating gene expression at transcriptional or post-transcriptional levels, modulating alternative splicing, and interacting with miRNAs or proteins. With the increasing amount of circRNA data, several databases have been established to organize and manage this information, such as circBase, CIRCpedia, CircAtlas, circRNADb, PlantCircNet, and CircFunBase. These diverse databases will help to explore circRNA characterization, and further investigate circRNA functions. In this chapter, we give a brief overview of the existing circRNA databases and focus on plant circRNA databases, introducing their key features.

Hints From the Cellular Functions to the Practical Outlook of Circular RNAs

Although it has been about 30 years since the discovery of circular RNAs (circRNAs) in mammalian cells, these subtypes of RNAs' capabilities have come into focus in recent years. The unique structure and various functional roles of circRNAs in many cellular processes have aroused researchers' interest and raised many questions about whether circRNAs can facilitate the diagnosis and treatment of diseases. To answer these questions, we will illustrate the main known functions and regulatory roles of circRNAs in the cell after presenting a brief history of the discovery of circRNAs and the main proposed theories of the biogenesis of circRNAs. Afterward, the practical application of circRNAs as biomarkers of different pathophysiological conditions will be discussed, mentioning some examples and challenges in this area. We also consider one of the main questions that human beings have always been faced, "the origin of life," and its possible connection to circRNAs. Finally, focusing on the various capabilities of circRNAs, we discuss their potential therapeutic applications considering the immunity response toward exogenous circRNAs. However, there are still disputes about the exact immune system reaction, which we will discuss in detail.

NoRCE: non-coding RNA sets cis enrichment tool

BACKGROUND

While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint at a functional association.

RESULTS

We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast.

CONCLUSIONS

NoRCE is a platform-independent, user-friendly, comprehensive R package that can be used to gain insight into the functional importance of a list of ncRNAs of any type. The tool offers flexibility to conduct the users' preferred set of analyses by designing their own pipeline of analysis. NoRCE is available in Bioconductor and https://github.com/guldenolgun/NoRCE .

Efficient deletion of multiple circle RNA loci by CRISPR-Cas9 reveals Os06circ02797 as a putative sponge for OsMIR408 in rice

CRISPR-Cas9 is an emerging genome editing tool for reverse genetics in plants. However, its application for functional study of non-coding RNAs in plants is still at its infancy. Despite being a major class of non-coding RNAs, the biological roles of circle RNAs (circRNAs) remain largely unknown in plants. Previous plant circRNA studies have focused on identification and annotation of putative circRNAs, with their functions largely uninvestigated by genetic approaches. Here, we applied a multiplexed CRISPR-Cas9 strategy to efficiently acquire individual null mutants for four circRNAs in rice. We showed each of these rice circRNA loci (Os02circ25329, Os06circ02797, Os03circ00204 and Os05circ02465) can be deleted at 10% or higher efficiency in both protoplasts and stable transgenic T0 lines. Such high efficiency deletion enabled the generation of circRNA null allele plants without the CRISPR-Cas9 transgene in the T1 generation. Characterization of the mutants reveals these circRNAs' participation in salt stress response during seed germination and in particular the Os05circ02465 null mutant showed high salt tolerance. Notably, the seedlings of the Os06circ02797 mutant showed rapid growth phenotype after seed germination with the seedlings containing higher chlorophyll A/B content. Further molecular and computational analyses suggested a circRNA-miRNA-mRNA regulatory network where Os06circ02797 functions to bind and sequester OsMIR408, an important and conserved microRNA in plants. This study not only presents genetic evidence for the first time in plants that certain circRNAs may serve as sponges to negatively regulate miRNAs, a phenomenon previously demonstrated in mammalian cells, but also provides important insights for improving agronomic traits through gene editing of circRNA loci in crops.

NeuroCirc: An Integrative Resource of Circular RNA Expression In the Human Brain

MOTIVATION

CircRNAs are covalently closed RNA molecules that are particularly abundant in the brain. While circRNA expression data from the human brain is rapidly accumulating, integration of large-scale datasets remains challenging and time-consuming, and consequently an integrative view of circRNA expression in the human brain is currently lacking.

RESULTS

NeuroCirc is a web-based resource that allows interactive exploration of multiple types of circRNA data from the human brain, including large-scale expression datasets, circQTL data and circRNA expression across neuronal differentiation and cellular maturation time-courses. NeuroCirc also allows users to upload their own circRNA expression data and explore it in the integrative platform, thereby supporting circRNA prioritization for experimental validation and functional studies. NeuroCirc is freely available at: https://voineagulab.github.io/NeuroCirc/. The source code and user documentation are available at: https://github.com/Voineagulab/NeuroCirc.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cancer-Associated circRNA-miRNA-mRNA Regulatory Networks: A Meta-Analysis

Recent advances in sequencing technologies and the discovery of non-coding RNAs (ncRNAs) have provided new insights in the molecular pathogenesis of cancers. Several studies have implicated the role of ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and recently discovered circular RNAs (circRNAs) in tumorigenesis and metastasis. Unlike linear RNAs, circRNAs are highly stable and closed-loop RNA molecules. It has been established that circRNAs regulate gene expression by controlling the functions of miRNAs and RNA-binding protein (RBP) or by translating into proteins. The circRNA-miRNA-mRNA regulatory axis is associated with human diseases, such as cancers, Alzheimer's disease, and diabetes. In this study, we explored the interaction among circRNAs, miRNAs, and their target genes in various cancers using state-of-the-art bioinformatics tools. We identified differentially expressed circRNAs, miRNAs, and mRNAs on multiple cancers from publicly available data. Furthermore, we identified many crucial drivers and tumor suppressor genes in the circRNA-miRNA-mRNA regulatory axis in various cancers. Together, this study data provide a deeper understanding of the circRNA-miRNA-mRNA regulatory mechanisms in cancers.

iCircDA-LTR: identification of circRNA-disease associations based on Learning to Rank

MOTIVATION

Due to the inherent stability and close relationship with the progression of diseases, circRNAs are serving as important biomarkers and drug targets. Efficient predictors for identifying circRNA-disease associations are highly required. The existing predictors consider circRNA-disease association prediction as a classification task or a recommendation problem, failing to capture the ranking information among the associations and detect the diseases associated with new circRNAs. However, more and more circRNAs are discovered. Identification of the diseases associated with these new circRNAs remains a challenging task.

RESULTS

In this study, we proposed a new predictor called iCricDA-LTR for circRNA-disease association prediction. Different from any existing predictor, iCricDA-LTR employed a ranking framework to model the global ranking associations among the query circRNAs and the diseases. The Learning to Rank (LTR) algorithm was employed to rank the associations based on various predictors and features in a supervised manner. The experimental results on two independent test datasets showed that iCircDA-LTR outperformed the other competing methods, especially for predicting the diseases associated with new circRNAs. As a result, iCircDA-LTR is more suitable for the real world applications.

AVAILABILITY

For the convenience of researchers to detect new circRNA-disease associations. The web server of iCircDA-LTR was established and freely available at http://bliulab.net/iCircDA-LTR/.

Circular RNAs and Hepatocellular Carcinoma: New Epigenetic Players With Diagnostic and Prognostic Roles

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Due to the lack of potent diagnosis and prognosis biomarkers and effective therapeutic targets, the overall prognosis of survival is poor in HCC patients. Circular RNAs (circRNAs) are a class of novel endogenous non-coding RNAs with covalently closed loop structures and implicated in diverse physiological processes and pathological diseases. Recent studies have demonstrated the involvement of circRNAs in HCC diagnosis, prognosis, development, and drug resistance, suggesting that circRNAs may be a class of novel targets for improving HCC diagnosis, prognosis, and treatments. In fact, some artificial circRNAs have been engineered and showed their therapeutic potential in treating HCV infection and gastric cancer. In this review, we introduce the potential of circRNAs as biomarkers for HCC diagnosis and prognosis, as therapeutic targets for HCC treatments and discuss the challenges in circRNA research and chances of circRNA application.

Database Resources for Functional Circular RNAs

Circular RNA (or circRNA) is a type of single-stranded covalently closed circular RNA molecule and play important roles in diverse biological pathways. A comprehensive functionally annotated circRNA database will help to understand the circRNAs and their functions. CircFunBase is such a web-accessible database that aims to provide a high-quality functional circRNA resource including experimentally validated and computationally predicted functions. CircFunBase provides visualized circRNA-miRNA interaction networks. In addition, a genome browser is provided to visualize the genome context of circRNA. In this chapter, we illustrate examples of searching for circRNA and getting detailed information of circRNA. Moreover, other circRNA related databases are outlined.

The bioinformatics toolbox for circRNA discovery and analysis

Circular RNAs (circRNAs) are a unique class of RNA molecule identified more than 40 years ago which are produced by a covalent linkage via back-splicing of linear RNA. Recent advances in sequencing technologies and bioinformatics tools have led directly to an ever-expanding field of types and biological functions of circRNAs. In parallel with technological developments, practical applications of circRNAs have arisen including their utilization as biomarkers of human disease. Currently, circRNA-associated bioinformatics tools can support projects including circRNA annotation, circRNA identification and network analysis of competing endogenous RNA (ceRNA). In this review, we collected about 100 circRNA-associated bioinformatics tools and summarized their current attributes and capabilities. We also performed network analysis and text mining on circRNA tool publications in order to reveal trends in their ongoing development.

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.

Closing the circle: current state and perspectives of circular RNA databases

Circular RNAs (circRNAs) are covalently closed RNA molecules that have been linked to various diseases, including cancer. However, a precise function and working mechanism are lacking for the larger majority. Following many different experimental and computational approaches to identify circRNAs, multiple circRNA databases were developed as well. Unfortunately, there are several major issues with the current circRNA databases, which substantially hamper progression in the field. First, as the overlap in content is limited, a true reference set of circRNAs is lacking. This results from the low abundance and highly specific expression of circRNAs, and varying sequencing methods, data-analysis pipelines, and circRNA detection tools. A second major issue is the use of ambiguous nomenclature. Thus, redundant or even conflicting names for circRNAs across different databases contribute to the reproducibility crisis. Third, circRNA databases, in essence, rely on the position of the circRNA back-splice junction, whereas alternative splicing could result in circRNAs with different length and sequence. To uniquely identify a circRNA molecule, the full circular sequence is required. Fourth, circRNA databases annotate circRNAs' microRNA binding and protein-coding potential, but these annotations are generally based on presumed circRNA sequences. Finally, several databases are not regularly updated, contain incomplete data or suffer from connectivity issues. In this review, we present a comprehensive overview of the current circRNA databases and their content, features, and usability. In addition to discussing the current issues regarding circRNA databases, we come with important suggestions to streamline further research in this growing field.

Methods for Predicting CircRNA-miRNA-mRNA Regulatory Networks: GreenCircRNA and PlantCircNet Databases as Study Cases

Circular RNAs are molecules formed by 3'-5' ligation in a splicing reaction, the so-called backsplicing. Well described in other groups, especially in humans, circRNA studies that include prediction and validation in plants are recent. It has already been shown that circRNAs can interact with microRNAs, acting as sponges, and adding a new layer of complexity in regulating eukaryotic transcription. Here, we cover two up-to-date databases that allow the users to perform analyses of the circRNA-miRNA-mRNA interactions in plants. We choose two databases to demonstrate their functions and compare their approaches to obtain a more robust and reliable interaction network.

A comprehensive survey on computational methods of non-coding RNA and disease association prediction

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Computational methods for annotation of plant regulatory non-coding RNAs using RNA-seq

Plant transcriptome encompasses numerous endogenous, regulatory non-coding RNAs (ncRNAs) that play a major biological role in regulating key physiological mechanisms. While studies have shown that ncRNAs are extremely diverse and ubiquitous, the functions of the vast majority of ncRNAs are still unknown. With ever-increasing ncRNAs under study, it is essential to identify, categorize and annotate these ncRNAs on a genome-wide scale. The use of high-throughput RNA sequencing (RNA-seq) technologies provides a broader picture of the non-coding component of transcriptome, enabling the comprehensive identification and annotation of all major ncRNAs across samples. However, the detection of known and emerging class of ncRNAs from RNA-seq data demands complex computational methods owing to their unique as well as similar characteristics. Here, we discuss major plant endogenous, regulatory ncRNAs in an RNA sample followed by computational strategies applied to discover each class of ncRNAs using RNA-seq. We also provide a collection of relevant software packages and databases to present a comprehensive bioinformatics toolbox for plant ncRNA researchers. We assume that the discussions in this review will provide a rationale for the discovery of all major categories of plant ncRNAs.

Cerina: systematic circRNA functional annotation based on integrative analysis of ceRNA interactions

Circular RNAs, a family of covalently circularized RNAs with tissue-specific expression, were recently demonstrated to play important roles in mammalian biology. Regardless of extensive research to predict, quantify, and annotate circRNAs, our understanding of their functions is still in its infancy. In this study, we developed a novel computational tool: Competing Endogenous RNA for INtegrative Annotations (Cerina), to predict biological functions of circRNAs based on the competing endogenous RNA model. Pareto Frontier Analysis was employed to integrate ENCODE mRNA/miRNA data with predicted microRNA response elements to prioritize tissue-specific ceRNA interactions. Using data from several circRNA-disease databases, we demonstrated that Cerina significantly improved the functional relevance of the prioritized ceRNA interactions by several folds, in terms of precision and recall. Proof-of-concept studies on human cancers and cardiovascular diseases further showcased the efficacy of Cerina on predicting potential circRNA functions in human diseases.

Seeing Is Believing: Visualizing Circular RNAs

Advancement in the RNA sequencing techniques has discovered hundreds of thousands of circular RNAs (circRNAs) in humans. However, the physiological function of most of the identified circRNAs remains unexplored. Recent studies have established that spliceosomal machinery and RNA-binding proteins modulate circRNA biogenesis. Furthermore, circRNAs have been implicated in regulating crucial cellular processes by interacting with various proteins and microRNAs. However, there are several challenges in understanding the mechanism of circRNA biogenesis, transport, and their interaction with cellular factors to regulate cellular events because of their low abundance and sequence similarity with linear RNA. Addressing these challenges requires systematic studies that directly visualize the circRNAs in cells at single-molecule resolution along with the molecular regulators. In this review, we present the design, benefits, and weaknesses of RNA imaging techniques such as single-molecule RNA fluorescence in situ hybridization and BaseScope in fixed cells and fluorescent RNA aptamers in live-cell imaging of circRNAs. Furthermore, we propose the potential use of molecular beacons, multiply labeled tetravalent RNA imaging probes, and Cas-derived systems to visualize circRNAs.

iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion

Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical roles in various biological processes, and the aberrant expression of circRNAs is closely related to tumorigenesis. Therefore, identifying potential circRNA-disease associations is beneficial to understand the pathogenesis of complex diseases at the circRNA level and helps biomedical researchers and practitioners to discover diagnostic biomarkers accurately. However, it is tremendously laborious and time-consuming to discover disease-related circRNAs with conventional biological experiments. In this study, we develop an integrative framework, called iCDA-CMG, to predict potential associations between circRNAs and diseases. By incorporating multi-source prior knowledge, including known circRNA-disease associations, disease similarities and circRNA similarities, we adopt a collective matrix completion-based graph learning model to prioritize the most promising disease-related circRNAs for guiding laborious clinical trials. The results show that iCDA-CMG outperforms other state-of-the-art models in terms of cross-validation and independent prediction. Moreover, the case studies for several representative cancers suggest the effectiveness of iCDA-CMG in screening circRNA candidates for human diseases, which will contribute to elucidating the pathogenesis mechanisms and unveiling new opportunities for disease diagnosis and targeted therapy.

The Potential of Circular RNAs as Cancer Biomarkers

Circular RNA (circRNA) is a covalently closed RNA structure that has several proposed functions related to cancer development. Recently, cancer-specific and tissue-specific circRNAs have been identified by high-throughput sequencing and are curated in publicly available databases. CircRNAs have features that are ideal properties of biomarkers, including conservation, abundance, and stability in plasma, saliva, and urine. Many circRNAs with predictive and prognostic significance in cancer have been described, and functional mechanisms for some circRNAs have been suggested. CircRNA also has great potential as a noninvasive biomarker for early cancer detection, although further investigation is necessary before clinical application is feasible.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Circular RNAs Are Regulators of Diverse Animal Transcriptomes: One Health Perspective

Derived from linear (parental) precursor mRNA, circRNA are recycled exons and introns whose ends are ligated. By titrating microRNAs and RNA binding proteins, circRNA interconnect networks of competing endogenous RNAs. Without altering chromosomal DNA, circRNA regulates skeletal muscle development and proliferation, lactation, ovulation, brain development, and responses to infections and metabolic stress. This review integrates emerging knowledge of circRNA activity coming from genome-wide characterizations in many clades of animals. circRNA research addresses one of the main pillars of the One Health vision – to improve the health and productivity of food animals and generate translational knowledge in animal species.

Disease-Associated Circular RNAs: From Biology to Computational Identification

Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer's disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.

Regulatory roles of circRNAs in adipogenesis and lipid metabolism: emerging insights into lipid-related diseases

Disorder of lipid metabolism has become an urgent health problem that brings about a variety of metabolic syndromes, including hepatic steatosis, adipose tissue dysfunction, diabetes and obesity. Circular RNAs (circRNAs), a class of emerging RNA molecules with unique structure and extensive effects, have been verified to participate in various biological programs through distinct mechanisms, especially in lipid-related processes. In this review, the biogenesis, characteristics, and functional mechanisms of circRNAs are discussed. Furthermore, the methods for circRNA identification and expression profiles of circRNAs associated with adipogenesis and lipid metabolism are described. Additionally, we emphasize the regulatory roles of circRNAs in adipogenesis, lipid metabolism, and lipid-related diseases. Finally, the diagnostic and therapeutic potential of circRNAs is highlighted, showing potential for the clinical application of circRNAs in the treatment of lipid-related diseases in the near future.

Aberrant expression of a novel circular RNA in pancreatic cancer

Circular RNAs (circRNAs) are single-stranded, covalently closed RNA molecules that are produced from pre-mRNAs through a process known as back-splicing. Although circRNAs are expressed under specific conditions, current understanding of their comprehensive expression status is still limited. Here, we performed a large-scale circRNA profiling analysis in human pancreatic ductal adenocarcinoma (PDAC) tissues, using circular RNA-specific RNA sequencing. We identified more than 40,000 previously unknown circRNAs, some of which were upregulated in PDAC tissues, compared with normal pancreatic tissues. We determined the full-length sequence of a circRNA upregulated in PDAC, which was derived from two noncoding RNA loci on chromosome 12. The novel circRNA, named circPDAC RNA, was not expressed in normal human cells, but was expressed in PDAC and other carcinoma cells. While postulated biological functions, such as peptide production from the circPDAC RNA, were not detected, its aberrant expression was confirmed in other PDAC tissues and in serum from a PDAC patient. These results demonstrate that comprehensive studies are necessary to reveal the expression status of circRNAs and that the circPDAC RNA identified here might serve as a novel biomarker for cancers, including PDAC.

Prediction of the differentially expressed circRNAs to decipher their roles in the onset of human colorectal cancers

Circular RNAs belong to the class of endogenous long non-coding RNAs that play important roles in many physiological processes including tumorigenesis. One such process is the onset of colorectal cancers (CRC) which is one of the most prevalent cancers in the world. However, the involvement of the circRNAs in CRC progression is still obscure. In this study, we screened the differentially expressed circRNAs in CRC by taking 10 pairs of tumor and non-tumor transcriptomic data. Datasets were downloaded from EBI ENA database and differential expression analysis was performed. For functional characterization and pathway enrichment of differentially expressed circRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed. Interactions with miRNAs and RNA binding proteins (RBPs) were predicted using miRanda, miRTarBase and starBase tools respectively. Our results identified total of 122 differentially expressed circRNAs in CRC onset, including 85 upregulated and 37 downregulated. GO and KEGG analyses revealed these circRNAs to be involved in many tumorigenic pathways. In addition, we predicted many miRNA and RBP targets of significantly expressed circRNAs that could exhibit the functional role in CRC progression. Combined analyses of miRanda, miRTarBase and KEGG pathway suggested that the possibly affected genes by circRNA-miRNA sponge to be associated with many cancer related pathways. From our findings we concluded 16 novel differentially expressed circRNAs that could play important roles in carcinogenesis of CRC. Our findings provide new insights in circRNA research and could therefore be useful in the development of potential biomarker and therapeutic approaches for CRC.

Circular RNAs in Sepsis: Biogenesis, Function, and Clinical Significance

Sepsis is a life-threatening condition that occurs when the body responds to an infection that damages it is own tissues. The major problem in sepsis is rapid, vital status deterioration in patients, which can progress to septic shock with multiple organ failure if not properly treated. As there are no specific treatments, early diagnosis is mandatory to reduce high mortality. Despite more than 170 different biomarkers being postulated, early sepsis diagnosis and prognosis remain a challenge for clinicians. Recent findings propose that circular RNAs (circRNAs) may play a prominent role in regulating the patients’ immune system against different pathogens, including bacteria and viruses. Mounting evidence also suggests that the misregulation of circRNAs is an early event in a wide range of diseases, including sepsis. Despite circRNA levels being altered in sepsis, the specific mechanisms controlling the dysregulation of these noncoding RNAs are not completely elucidated, although many factors are known to affect circRNA biogenesis. Therefore, there is a need to explore the molecular pathways that lead to this disorder. This review describes the role of this new class of regulatory RNAs in sepsis and the feasibility of using circRNAs as diagnostic biomarkers for sepsis, opening up new avenues for circRNA-based medicine.

Characterization and Function of Circular RNAs in Plants

CircRNAs are covalently closed-loop single-stranded RNA molecules ubiquitously expressing in eukaryotes. As an important member of the endogenous ncRNA family, circRNAs are associated with diverse biological processes and can regulate transcription, modulate alternative splicing, and interact with miRNAs or proteins. Compared to abundant advances in animals, studies of circRNAs in plants are rapidly emerging. The databases and analysis tools for plant circRNAs are constantly being developed. Large numbers of circRNAs have been identified and characterized in plants and proved to play regulatory roles in plant growth, development, and stress responses. Here, we review the biogenesis, characteristics, bioinformatics resources, and biological functions of plant circRNAs, and summarize the distinct circularization features and differentially expression patterns comparison with animal-related results.

iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation

Found in recent research, tumor cell invasion, proliferation, or other biological processes are controlled by circular RNA. Understanding the association between circRNAs and diseases is an important way to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Most methods, such as k-mer and PSSM, based on the analysis of high-throughput expression data have the tendency to think functionally similar nucleic acid lack direct linear homology regardless of positional information and only quantify nonlinear sequence relationships. However, in many complex diseases, the sequence nonlinear relationship between the pathogenic nucleic acid and ordinary nucleic acid is not much different. Therefore, the analysis of positional information expression can help to predict the complex associations between circRNA and disease. To fill up this gap, we propose a new method, named iCDA-CGR, to predict the circRNA-disease associations. In particular, we introduce circRNA sequence information and quantifies the sequence nonlinear relationship of circRNA by Chaos Game Representation (CGR) technology based on the biological sequence position information for the first time in the circRNA-disease prediction model. In the cross-validation experiment, our method achieved 0.8533 AUC, which was significantly higher than other existing methods. In the validation of independent data sets including circ2Disease, circRNADisease and CRDD, the prediction accuracy of iCDA-CGR reached 95.18%, 90.64% and 95.89%. Moreover, in the case studies, 19 of the top 30 circRNA-disease associations predicted by iCDA-CGR on circRDisease dataset were confirmed by newly published literature. These results demonstrated that iCDA-CGR has outstanding robustness and stability, and can provide highly credible candidates for biological experiments.

Understanding the association between circRNAs and diseases is an important step to explore the pathogenesis of complex diseases and promote disease-targeted therapy. Computational methods contribute to discovering the potential disease-related circRNAs. Based on the analysis of the location information expression of biological sequences, the model of iCDA-CGR is proposed to predict the circRNA-disease associations by integrates multi-source information, including circRNA sequence information, gene-circRNA associations information, circRNA-disease associations information and the disease semantic information. In particular, the location information of circRNA sequences was first introduced into the circRNA-disease associations prediction model. The promising results on cross-validation and independent data sets demonstrated the effectiveness of the proposed model. We further implemented case studies, and 19 of the top 30 predicted scores of the proposed model were confirmed by recent experimental reports. The results show that iCDA-CGR model can effectively predict the potential circRNA-disease associations and provide highly reliable candidates for biological experiments, thus helping to further understand the complex disease mechanism.

Circular RNAs and their participation in stemness of cancer

Circular RNAs (circRNA) are covalently closed single-stranded RNA molecules that act as important regulators of gene expression through different mechanisms. Meanwhile, cancer stem cells (CSCs) are a small subpopulation of cells, with properties similar to normal stem cells that arise during the development of cancer and support tumor growth, induce resistance to therapy, and are responsible for metastatic spread. Since the elimination of CSCs is an important goal of cancer treatment, the circRNAs that participate in regulating gene expression and signaling pathways linked to CSCs have aroused attention in recent years, especially because it has been suggested that these molecules may function as therapeutic targets and/or clinical biomarkers. Thus, the proposal of this work is to enumerate a series of circRNAs that have been shown to play a relevant role in CSCs and explain in detail the molecular regulatory mechanisms that they establish to perform that function.

The Regulatory Functions of Circular RNAs in Digestive System Cancers

Circular ribonucleic acids (circRNAs), which are a type of covalently closed circular RNA, are receiving increasing attention. An increasing amount of evidence suggests that circRNAs are involved in the biogenesis and development of multiple diseases such as digestive system cancers. Dysregulated circRNAs have been found to act as oncogenes or tumour suppressors in digestive system cancers. Moreover, circRNAs are related to ageing and a wide variety of processes in tumour cells, such as cell apoptosis, invasion, migration, and proliferation. Moreover, circRNAs can perform a remarkable multitude of biological functions, such as regulating splicing or transcription, binding RNA-binding proteins to enable function, acting as microRNA (miRNA) sponges, and undergoing translated into proteins. However, in digestive system cancers, circRNAs function mainly as miRNA sponges. Herein, we summarise the latest research progress on biological functions of circRNAs in digestive system cancers. This review serves as a synopsis of potential therapeutic targets and biological markers for digestive system cancer.

A New World of Biomarkers and Therapeutics for Female Reproductive System and Breast Cancers: Circular RNAs

As one of the most recently (re)discovered types of non-coding RNAs (ncRNA), circular RNAs (circRNAs) differentiate from other ncRNAs by a specific biogenesis, high stability, and distinct functions. The biogenesis of circRNAs can be categorized into three mechanisms that permit the back-splicing reaction: exon-skipping, pairing of neighboring introns, and dimerization of RNA-binding proteins. Regarding their stability, circRNAs have no free ends, specific to linear RNA molecules, prompting a longer half-life and resistance to exonuclease-mediated activity by RNase R, bypassing the common RNA turnover process. Regarding their functions, circular transcripts can be categorized into four broad roles: miRNA sponging, protein binding, regulation of transcription, and coding for proteins and peptides. Female reproductive system (including mainly ovarian, corpus, and cervix uteri cancers) and breast cancers are the primary causes of death in women worldwide, accounting for over 1,212,772 deaths in 2018. We consider that a better understanding of the molecular pathophysiology through the study of coding and non-coding RNA regulators could improve the diagnosis and therapeutics of these cancers. Developments in the field of circRNA in regard to breast or gynecological cancers are recent, with most circRNA-related discoveries having been made in the last 2 years. Therefore, in this review we summarize the newly detected roles of circRNAs in female reproductive system (cervical cancer, ovarian cancer, and endometrial cancer) and breast cancers. We argue that circRNAs can become essential elements of the diagnostic and therapeutic tools for female reproductive system cancers in the future.

circRNAs Signature as Potential Diagnostic and Prognostic Biomarker for Diabetes Mellitus and Related Cardiovascular Complications

Circular RNAs (circRNAs) belong to the ever-growing class of naturally occurring noncoding RNAs (ncRNAs) molecules. Unlike linear RNA, circRNAs are covalently closed transcripts mostly generated from precursor-mRNA by a non-canonical event called back-splicing. They are highly stable, evolutionarily conserved, and widely distributed in eukaryotes. Some circRNAs are believed to fulfill a variety of functions inside the cell mainly by acting as microRNAs (miRNAs) or RNA-binding proteins (RBPs) sponges. Furthermore, mounting evidence suggests that the misregulation of circRNAs is among the first alterations in various metabolic disorders including obesity, hypertension, and cardiovascular diseases. More recent research has revealed that circRNAs also play a substantial role in the pathogenesis of diabetes mellitus (DM) and related vascular complications. These findings have added a new layer of complexity to our understanding of DM and underscored the need to reexamine the molecular pathways that lead to this disorder in the context of epigenetics and circRNA regulatory mechanisms. Here, I review current knowledge about circRNAs dysregulation in diabetes and describe their potential role as innovative biomarkers to predict diabetes-related cardiovascular (CV) events. Finally, I discuss some of the actual limitations to the promise of these RNA transcripts as emerging therapeutics and provide recommendations for future research on circRNA-based medicine.

Circular RNAs-The Road Less Traveled

Circular RNAs are the most recent addition in the non-coding RNA family, which has started to gain recognition after a decade of obscurity. The first couple of reports that emerged at the beginning of this decade and the amount of evidence that has accumulated thereafter has, however, encouraged RNA researchers to navigate further in the quest for the exploration of circular RNAs. The joining of 5′ and 3′ ends of RNA molecules through backsplicing forms circular RNAs during co-transcriptional or post-transcriptional processes. These molecules are capable of effectively sponging microRNAs, thereby regulating the cellular processes, as evidenced by numerous animal and plant systems. Preliminary studies have shown that circular RNA has an imperative role in transcriptional regulation and protein translation, and it also has significant therapeutic potential. The high stability of circular RNA is rendered by its closed ends; they are nevertheless prone to degradation by circulating endonucleases in serum or exosomes or by microRNA-mediated cleavage due to their high complementarity. However, the identification of circular RNAs involves diverse methodologies and the delineation of its possible role and mechanism in the regulation of cellular and molecular architecture has provided a new direction for the continuous research into circular RNA. In this review, we discuss the possible mechanism of circular RNA biogenesis, its structure, properties, degradation, and the growing amount of evidence regarding the detection methods and its role in animal and plant systems.

PncStress: a manually curated database of experimentally validated stress-responsive non-coding RNAs in plants

Non-coding RNAs (ncRNAs) are recognized as key regulatory molecules in many biological processes. Accumulating evidence indicates that ncRNA-related mechanisms play important roles in plant stress responses. Although abundant plant stress-responsive ncRNAs have been identified, these experimentally validated results have not been gathered into a single public domain archive. Therefore, we established PncStress by curating experimentally validated stress-responsive ncRNAs in plants, including microRNAs, long non-coding RNAs and circular RNAs. The current version of PncStress contains 4227 entries from 114 plants covering 48 biotic and 91 abiotic stresses. For each entry, PncStress has biological information and network visualization. Serving as a manually curated database, PncStress will become a valuable resource in support of plant stress response research.

CircR2Cancer: a manually curated database of associations between circRNAs and cancers

Accumulating evidences have shown that the deregulation of circRNA has close association with many human cancers. However, these experimental verified circRNA–cancer associations are not collected in any database. Here, we develop a manually curated database (circR2Cancer) that provides experimentally supported associations between circRNAs and cancers. The current version of the circR2Cancer contains 1439 associations between 1135 circRNAs and 82 cancers by extracting data from existing literatures and databases. In addition, circR2Cancer contains the information of cancer exacted from Disease Ontology and basic biological information of circRNAs from circBase. At the same time, circR2Cancer provides a simple and friendly interface for users to conveniently browse, search and download the data. It will be a useful and valuable resource for researchers to understanding the regulation mechanism of circRNA in cancers.

Database URL

http://www.biobdlab.cn:8000

Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data

Recent improvements in cost-effectiveness of high-throughput technologies has allowed RNA sequencing of total transcriptomes suitable for evaluating the expression and regulation of circRNAs, a relatively novel class of transcript isoforms with suggested roles in transcriptional and post-transcriptional gene expression regulation, as well as their possible use as biomarkers, due to their deregulation in various human diseases. A limited number of integrated workflows exists for prediction, characterization, and differential expression analysis of circRNAs, none of them complying with computational reproducibility requirements. We developed Docker4Circ for the complete analysis of circRNAs from RNA-Seq data. Docker4Circ runs a comprehensive analysis of circRNAs in human and model organisms, including: circRNAs prediction; classification and annotation using six public databases; back-splice sequence reconstruction; internal alternative splicing of circularizing exons; alignment-free circRNAs quantification from RNA-Seq reads; and differential expression analysis. Docker4Circ makes circRNAs analysis easier and more accessible thanks to: (i) its R interface; (ii) encapsulation of computational tasks into docker images; (iii) user-friendly Java GUI Interface availability; and (iv) no need of advanced bash scripting skills for correct use. Furthermore, Docker4Circ ensures a reproducible analysis since all its tasks are embedded into a docker image following the guidelines provided by Reproducible Bioinformatics Project.

Small noncoding RNA discovery and profiling with sRNAtools based on high-throughput sequencing

Small noncoding RNAs (sRNA/sncRNAs) are generated from different genomic loci and play important roles in biological processes, such as cell proliferation and the regulation of gene expression. Next-generation sequencing (NGS) has provided an unprecedented opportunity to discover and quantify diverse kinds of sncRNA, such as tRFs (tRNA-derived small RNA fragments), phasiRNAs (phased, secondary, small-interfering RNAs), Piwi-interacting RNA (piRNAs) and plant-specific 24-nt short interfering RNAs (siRNAs). However, currently available web-based tools do not provide approaches to comprehensively analyze all of these diverse sncRNAs. This study presents a novel integrated platform, sRNAtools (https://bioinformatics.caf.ac.cn/sRNAtools), that can be used in conjunction with high-throughput sequencing to identify and functionally annotate sncRNAs, including profiling microRNAss, piRNAs, tRNAs, small nuclear RNAs, small nucleolar RNAs and rRNAs and discovering isomiRs, tRFs, phasiRNAs and plant-specific 24-nt siRNAs for up to 21 model organisms. Different modules, including single case, batch case, group case and target case, are developed to provide users with flexible ways of studying sncRNA. In addition, sRNAtools supports different ways of uploading small RNA sequencing data in a very interactive queue system, while local versions based on the program package/Docker/virtureBox are also available. We believe that sRNAtools will greatly benefit the scientific community as an integrated tool for studying sncRNAs.

Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

Circular RNAs: A Novel Class of Functional RNA Molecules with a Therapeutic Perspective

Circular RNAs (circRNAs) are a subclass of non-coding RNAs that lack free 3' and 5' ends and, thus, exist as continuous loop RNAs. Such circular transcripts have been identified for thousands of genes, are regulated in developmental stages and pathophysiological conditions, and are often expressed in a tissue- or cell-type-specific manner. For a long time, circular transcripts were considered as aberrant splicing by-products. However, high-throughput transcriptome sequencing and focused molecular characterization of individual circRNAs uncovered their ubiquity. Evidence emerges suggesting circRNAs are functional molecules. In this review, we illustrate the current knowledge of circRNA formation and circRNA detection methods. We summarize different molecular mechanisms of action and highlight circRNAs with specific roles in cardiovascular disease. Finally, we describe a number of tools for circRNA manipulation, which may be exploited for circRNA-based therapeutic interventions in the future.