PlantCircNet: a database for plant circRNA-miRNA-mRNA regulatory networks

PlantCircRNA: a comprehensive database for plant circular RNAs

Circular RNAs (circRNAs) represent recently discovered novel regulatory non-coding RNAs. While they are present in many eukaryotes, there has been limited research on plant circRNAs. We developed PlantCircRNA (https://plant.deepbiology.cn/PlantCircRNA/) to fill this gap. The two most important features of PlantCircRNA are (i) it incorporates circRNAs from 94 plant species based on 39 245 RNA-sequencing samples and (ii) it imports the original AtCircDB and CropCircDB databases. We manually curated all circRNAs from published articles, and imported them into the database. Furthermore, we added detailed information of tissue as well as abiotic stresses to the database. To help users understand these circRNAs, the database includes a detection score to measure their consistency and a naming system following the guidelines recently proposed for eukaryotes. Finally, we developed a comprehensive platform for users to visualize, analyze, and download data regarding specific circRNAs. This resource will serve as a home for plant circRNAs and provide the community with unprecedented insights into these mysterious molecule.

Advances in CircRNAs in the Past Decade: Review of CircRNAs Biogenesis, Regulatory Mechanisms, and Functions in Plants

Circular RNA (circRNA) is a type of non-coding RNA with multiple biological functions. Whole circRNA genomes in plants have been identified, and circRNAs have been demonstrated to be widely present and highly expressed in various plant tissues and organs. CircRNAs are highly stable and conserved in plants, and exhibit tissue specificity and developmental stage specificity. CircRNAs often interact with other biomolecules, such as miRNAs and proteins, thereby regulating gene expression, interfering with gene function, and affecting plant growth and development or response to environmental stress. CircRNAs are less studied in plants than in animals, and their regulatory mechanisms of biogenesis and molecular functions are not fully understood. A variety of circRNAs in plants are involved in regulating growth and development and responding to environmental stress. This review focuses on the biogenesis and regulatory mechanisms of circRNAs, as well as their biological functions during growth, development, and stress responses in plants, including a discussion of plant circRNA research prospects. Understanding the generation and regulatory mechanisms of circRNAs is a challenging but important topic in the field of circRNAs in plants, as it can provide insights into plant life activities and their response mechanisms to biotic or abiotic stresses as well as new strategies for plant molecular breeding and pest control.

CircRNA: An emerging star in plant research: A review

CircRNAs are a class of covalently closed non-coding RNA formed by linking the 5' terminus and the 3' terminus after reverse splicing. CircRNAs are widely found in eukaryotes, and they are highly conserved, with spatio-temporal expression specificity and stability. CircRNAs can act as miRNA sponges to regulate the expression of downstream target genes, regulating the transcription of parental genes and some can even be translated into peptides or proteins. Research on circRNAs in plants is still in its infancy compared to that in animals. With the deepening of research, the results of a variety of plant circRNAs suggest that they play an important role in growth and development, and tolerance towards abiotic stresses such as salt, drought, low temperature, high temperature and other adverse environments. In this review paper, we elaborated the molecular characteristics, mechanism of action, function and bioinformatics databases of plant circRNAs, combined with the progress of circRNA research in animals, discussed the potential mechanism of action of plant circRNAs, and proposed the unsolved problems and prospects for future application of plant circRNAs.

Computational approaches and challenges in the analysis of circRNA data

Circular RNAs (circRNA) are a class of non-coding RNA, forming a single-stranded covalently closed loop structure generated via back-splicing. Advancements in sequencing methods and technologies in conjunction with algorithmic developments of bioinformatics tools have enabled researchers to characterise the origin and function of circRNAs, with practical applications as a biomarker of diseases becoming increasingly relevant. Computational methods developed for circRNA analysis are predicated on detecting the chimeric back-splice junction of circRNAs whilst mitigating false-positive sequencing artefacts. In this review, we discuss in detail the computational strategies developed for circRNA identification, highlighting a selection of tool strengths, weaknesses and assumptions. In addition to circRNA identification tools, we describe methods for characterising the role of circRNAs within the competing endogenous RNA (ceRNA) network, their interactions with RNA-binding proteins, and publicly available databases for rich circRNA annotation.

Functional Differences of Grapevine Circular RNA Vv-circPTCD1 in Arabidopsis and Grapevine Callus under Abiotic Stress

Circular RNAs (circRNAs) serve as covalently closed single-stranded RNAs and have been proposed to influence plant development and stress resistance. Grapevine is one of the most economically valuable fruit crops cultivated worldwide and is threatened by various abiotic stresses. Herein, we reported that a circRNA (Vv-circPTCD1) processed from the second exon of the pentatricopeptide repeat family gene PTCD1 was preferentially expressed in leaves and responded to salt and drought but not heat stress in grapevine. Additionally, the second exon sequence of PTCD1 was highly conserved, but the biogenesis of Vv-circPTCD1 is species-dependent in plants. It was further found that the overexpressed Vv-circPTCD1 can slightly decrease the abundance of the cognate host gene, and the neighboring genes are barely affected in the grapevine callus. Furthermore, we also successfully overexpressed the Vv-circPTCD1 and found that the Vv-circPTCD1 deteriorated the growth during heat, salt, and drought stresses in Arabidopsis. However, the biological effects on grapevine callus were not always consistent with those of Arabidopsis. Interestingly, we found that the transgenic plants of linear counterpart sequence also conferred the same phenotypes as those of circRNA during the three stress conditions, no matter what species it is. Those results imply that although the sequences are conserved, the biogenesis and functions of Vv-circPTCD1 are species-dependent. Our results indicate that the plant circRNA function investigation should be conducted in homologous species, which supports a valuable reference for further plant circRNA studies.

Role of non-coding RNAs against salinity stress in Oryza species: Strategies and challenges in analyzing miRNAs, tRFs and circRNAs

Salinity is an imbalanced concentration of mineral salts in the soil or water that causes yield loss in salt-sensitive crops. Rice plant is vulnerable to soil salinity stress at seedling and reproductive stages. Different non-coding RNAs (ncRNAs) post-transcriptionally regulate different sets of genes during different developmental stages under varying salinity tolerance levels. While microRNAs (miRNAs) are well known small endogenous ncRNAs, tRNA-derived RNA fragments (tRFs) are an emerging class of small ncRNAs derived from tRNA genes with a demonstrated regulatory role, like miRNAs, in humans but unexplored in plants. Circular RNA (circRNA), another ncRNA produced by back-splicing events, acts as target mimics by preventing miRNAs from binding with their target mRNAs, thereby reducing the miRNA's action upon its target. Same may hold true between circRNAs and tRFs. Hence, the work done on these ncRNAs was reviewed and no reports were found for circRNAs and tRFs under salinity stress in rice, either at seedling or reproductive stages. Even the reports on miRNAs are restricted to seedling stage only, in spite of severe effects on rice crop production due to salt stress during reproductive stage. Moreover, this review sheds light on strategies to predict and analyze these ncRNAs in an effective manner.

Identification, biogenesis, function, and mechanism of action of circular RNAs in plants

Circular RNAs (circRNAs) are a class of single-stranded, closed RNA molecules with unique functions that are ubiquitously expressed in all eukaryotes. The biogenesis of circRNAs is regulated by specific cis-acting elements and trans-acting factors in humans and animals. circRNAs mainly exert their biological functions by acting as microRNA sponges, forming R-loops, interacting with RNA-binding proteins, or being translated into polypeptides or proteins in human and animal cells. Genome-wide identification of circRNAs has been performed in multiple plant species, and the results suggest that circRNAs are abundant and ubiquitously expressed in plants. There is emerging compelling evidence to suggest that circRNAs play essential roles during plant growth and development as well as in the responses to biotic and abiotic stress. However, compared with recent advances in human and animal systems, the roles of most circRNAs in plants are unclear at present. Here we review the identification, biogenesis, function, and mechanism of action of plant circRNAs, which will provide a fundamental understanding of the characteristics and complexity of circRNAs in plants.

Biological functions and potential implications of circular RNAs

Circular RNAs (circRNAs) are characterized by a covalent closed-loop structure with an absence of both 5' cap structure and 3' polyadenylated tail. Numerous studies have found that circRNAs play an important role in various diseases and have a variety of biological regulatory mechanisms, including acting as microRNA sponges, interacting with proteins, modulating the expression of related genes and translating into peptides or proteins. CircRNAs have also been used as biomarkers for a number of diseases, which could improve clinical practice. This review summarizes the most recent advances in biogenesis and knowledge of the biological functions of circRNAs as well as the related bioinformatics databases. We specifically describe developments in understanding of circRNA functions in the field of environmental exposure-induced diseases. Finally, we focus on potential clinical implications of circRNAs to facilitate their clinical transformation into disease treatment.

Regulatory non-coding RNA: The core defense mechanism against plant pathogens

Plant pathogens damage crops and threaten global food security. Plants have evolved complex defense networks against pathogens, using crosstalk among various signaling pathways. Key regulators conferring plant immunity through signaling pathways include protein-coding genes and non-coding RNAs (ncRNAs). The discovery of ncRNAs in plant transcriptomes was first considered "transcriptional noise". Recent reviews have highlighted the importance of non-coding RNAs. However, understanding interactions among different types of noncoding RNAs requires additional research. This review attempts to consider how long-ncRNAs, small-ncRNAs and circular RNAs interact in response to pathogenic diseases within different plant species. Developments within genomics and bioinformatics could lead to the further discovery of plant ncRNAs, knowledge of their biological roles, as well as an understanding of their importance in exploiting the recent molecular-based technologies for crop protection.

Identification and Characterization of Circular RNAs Involved in the Flower Development and Senescence of Rhododendron delavayi Franch

Floral development and senescence are a crucial determinant for economic and ornamental value. CircRNAs play an essential role in regulating plant growth and development; however, there is no systematic identification of circRNAs during the lifespan of flowers. This study aims to explore the expression profile and functional role of circRNAs in the full flowering stages of Rhododendron delavayi Franch. We carried out transcriptome sequencing of the six stages of Rhododendron delavayi Franch flowers to identify the circular RNA expression profile. In addition, using bioinformatics methods, we explored the functions of circRNAs, including analysis of the circRNA-miRNA-mRNA network, short time-series expression miner (STEM), and so on. We identified 146 circRNAs, of which 79 were differentially expressed from the budding to fading stages. Furthermore, using STEM analysis, one of the 42 circRNA expression model profiles was significantly upregulated during the senescence stage, including 16 circRNAs. Additionally, 7 circRNA-miRNA-mRNA networks were constructed with 10 differentially expressed circRNAs, in which some target mRNA may regulate the development and senescence of the Rhododendron flowers. Finally, by analyzing the correlation between circRNAs and mRNA, combined with existing reports, we proposed that circRNAs play a regulatory role during flower development and senescence by mediating the jasmonate signaling pathway. Overall, these results provide new clues to the potential mechanism of circRNAs acting as novel post-transcriptional regulators in the development and senescence process of flowers.

PlantcircBase 7.0: Full-length transcripts and conservation of plant circRNAs

Circular RNA (circRNA) is a special type of non-coding RNA that participates in diverse biological processes in both animals and plants. Five years ago, we developed a comprehensive plant circRNA database (PlantcircBase), which has attracted much attention from the plant circRNA community. Here, we report an updated PlantcircBase (v.7.0), which contains 171,118 circRNAs from 21 plant species. Over 31,000 of the circRNAs have full-length sequences constructed based on analysis of 749 bulk RNA sequencing (RNA-seq) datasets downloaded from the public domain and Nanopore long-read sequencing results of rice RNAs newly generated in this study. A plant multiple conservation score (PMCS), based on the conservation of both sequence and expression profiles, was calculated for each circRNA to quantify and compare the conservation of all circRNAs. A new parameter, plant circRNA confidence level (PCCL), is introduced to measure the identity reliability of each circRNA based on experimental validation results and the number of references that support the circRNA. All this information and other details of circRNAs can be browsed, searched, and downloaded from PlantcircBase 7.0, which also provides online bioinformatics tools for visualization and sequence alignment. PlantcircBase 7.0 is publicly and freely accessible at http://ibi.zju.edu.cn/plantcircbase/.

CircR2Disease v2.0: An Updated Web Server for Experimentally Validated circRNA-disease Associations and Its Application

With accumulating dysregulated circular RNAs (circRNAs) in pathological processes, the regulatory functions of circRNAs, especially circRNAs as microRNA (miRNA) sponges and their interactions with RNA-binding proteins (RBPs), have been widely validated. However, the collected information on experimentally validated circRNA-disease associations is only preliminary. Therefore, an updated CircR2Disease database providing a comprehensive resource and web tool to clarify the relationships between circRNAs and diseases in diverse species is necessary. Here, we present an updated CircR2Disease v2.0 with the increased number of circRNA-disease associations and novel characteristics. CircR2Disease v2.0 provides more than 5-fold experimentally validated circRNA-disease associations compared to its previous version. This version includes 4201 entries between 3077 circRNAs and 312 disease subtypes. Secondly, the information of circRNA-miRNA, circRNA-miRNA-target, and circRNA-RBP interactions has been manually collected for various diseases. Thirdly, the gene symbols of circRNAs and disease name IDs can be linked with various nomenclature databases. Detailed descriptions such as samples and journals have also been integrated into the updated version. Thus, CircR2Disease v2.0 can serve as a platform for users to systematically investigate the roles of dysregulated circRNAs in various diseases and further explore the posttranscriptional regulatory function in diseases. Finally, we propose a computational method named circDis based on the graph convolutional network (GCN) and gradient boosting decision tree (GBDT) to illustrate the applications of the CircR2Disease v2.0 database. CircR2Disease v2.0 is available at http://bioinfo.snnu.edu.cn/CircR2Disease_v2.0 and https://github.com/bioinforlab/CircR2Disease-v2.0.

Biogenesis, Functions, Interactions, and Resources of Non-Coding RNAs in Plants

Plant transcriptomes encompass a large number of functional non-coding RNAs (ncRNAs), only some of which have protein-coding capacity. Since their initial discovery, ncRNAs have been classified into two broad categories based on their biogenesis and mechanisms of action, housekeeping ncRNAs and regulatory ncRNAs. With advances in RNA sequencing technology and computational methods, bioinformatics resources continue to emerge and update rapidly, including workflow for in silico ncRNA analysis, up-to-date platforms, databases, and tools dedicated to ncRNA identification and functional annotation. In this review, we aim to describe the biogenesis, biological functions, and interactions with DNA, RNA, protein, and microorganism of five major regulatory ncRNAs (miRNA, siRNA, tsRNA, circRNA, lncRNA) in plants. Then, we systematically summarize tools for analysis and prediction of plant ncRNAs, as well as databases. Furthermore, we discuss the silico analysis process of these ncRNAs and present a protocol for step-by-step computational analysis of ncRNAs. In general, this review will help researchers better understand the world of ncRNAs at multiple levels.

Computational Analysis of Transposable Elements and CircRNAs in Plants

This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.

MicroRNA-mediated bioengineering for climate-resilience in crops

Global projections on the climate change and the dynamic environmental perturbations indicate severe impacts on food security in general, and crop yield, vigor and the quality of produce in particular. Sessile plants respond to environmental challenges such as salt, drought, temperature, heavy metals at transcriptional and/or post-transcriptional levels through the stress-regulated network of pathways including transcription factors, proteins and the small non-coding endogenous RNAs. Amongs these, the miRNAs have gained unprecedented attention in recent years as key regulators for modulating gene expression in plants under stress. Hence, tailoring of miRNAs and their target pathways presents a promising strategy for developing multiple stress-tolerant crops. Plant stress tolerance has been successfully achieved through the over expression of microRNAs such as Os-miR408, Hv-miR82 for drought tolerance; OsmiR535A and artificial DST miRNA for salinity tolerance; and OsmiR535 and miR156 for combined drought and salt stress. Examples of miR408 overexpression also showed improved efficiency of irradiation utilization and carbon dioxide fixation in crop plants. Through this review, we present the current understanding about plant miRNAs, their roles in plant growth and stress-responses, the modern toolbox for identification, characterization and validation of miRNAs and their target genes including in silico tools, machine learning and artificial intelligence. Various approaches for up-regulation or knock-out of miRNAs have been discussed. The main emphasis has been given to the exploration of miRNAs for development of bioengineered climate-smart crops that can withstand changing climates and stressful environments, including combination of stresses, with very less or no yield penalties.

Genome-Wide Identification of Circular RNAs Potentially Involved in the Biosynthesis of Secondary Metabolites in Salvia miltiorrhiza

Circular RNAs (circRNAs) play various roles in cellular functions. However, no studies have been reported on the potential involvement of circRNAs in the biosynthesis of secondary metabolites in plants. Here, we performed a genome-wide discovery of circRNAs from root, stem and leaf samples of Salvia miltiorrhiza using RNA-Seq. We predicted a total of 2,476 circRNAs with at least two junction reads using circRNA_finder and CIRI, of which 2,096, 151 and 229 were exonic, intronic and intergenic circRNAs, respectively. Sequence similarity analysis showed that 294 out of 2,476 circRNAs were conserved amongst multiple plants. Of the 55 predicted circRNAs, 31 (56%) were validated successfully by PCR and Sanger sequencing using convergent and divergent primer pairs. Alternative circularisation analysis showed that most parental genes produced two circRNAs. Functional enrichment analyses of the parental genes showed that the primary metabolism pathways were significantly enriched, particularly the carbon metabolism. Differential expression analysis showed that the expression profiles of circRNAs were tissue-specific. Co-expression analysis showed 275 circRNAs, and their parental genes had significantly positive correlations. However, 14 had significantly negative correlations. Weighted gene co-expression network analysis showed that nine circRNAs were co-expressed with four modules of protein-coding genes. Next, we found 416 exonic circRNAs with miRNA-binding sites, suggesting possible interactions between circRNAs and miRNAs. Lastly, we found six validated circRNAs, namely, SMscf2473-46693-46978, SMscf3091-29256-29724, SMscf16-111773-112193, SMscf432-13232-13866, SMscf7007-10563-10888 and SMscf1730-1749-2013, which were originated from the genes involved in the biosynthesis of secondary metabolites. Their parental genes were acetyl-CoA C-acetyltransferase 1 (SmAACT1), 1-deoxy-d-xylulose-5-phosphate synthase 2 (SmDXS2), 4-hydroxy-3-methylbut-2-enyl diphosphate reductase 1 (SmHDR1), kaurene synthase-like 2 (SmKSL2), DWF4 and CYP88A3, respectively. In particular, the correlation coefficient of SMscf2473-46693-46978 and SmDXS2 gene was 0.86 (p = 0.003), indicating a potential interaction between this pair of circRNA and its parent gene. Our results provided the first comprehensive catalogue of circRNAs in S. miltiorrhiza and identified one circRNA that might play important roles in the biosynthesis of secondary metabolites.

At-C-RNA database, a one-stop source for information on circRNAs in Arabidopsis thaliana in a unified format

Circular RNAs (circRNAs) are a large class of noncoding RNAs with functions that, in most cases, remain unknown. Recent genome-wide analysis of circRNAs using RNA-Seq has revealed that circRNAs are abundant and some of them conserved in plants. Furthermore, it has been shown that the expression of circRNAs in plants is regulated in a tissue-specific manner. Arabidopsis thaliana circular RNA database is a new resource designed to integrate and standardize the data available for circRNAs in a model plant A. thaliana, which is currently the best-characterized plant in terms of circRNAs. The resource integrates all applicable publicly available RNA-seq datasets. These datasets were subjected to extensive reanalysis and curation, yielding results in a unified format. Moreover, all data were normalized according to our optimized approach developed for circRNA identification in plants. As a result, the database accommodates circRNAs identified across organs and seedlings of wild-type A. thaliana and its single-gene knockout mutants for genes related to splicing. The database provides free access to unified data and search functionalities, thus enabling comparative analyses of A. thaliana circRNAs between organs, variants and studies for the first time.

Database URLhttps://plantcircrna.ibch.poznan.pl/

Learning Representation of Molecules in Association Network for Predicting Intermolecular Associations

A key aim of post-genomic biomedical research is to systematically understand molecules and their interactions in human cells. Multiple biomolecules coordinate to sustain life activities, and interactions between various biomolecules are interconnected. However, existing studies usually only focusing on associations between two or very limited types of molecules. In this study, we propose a network representation learning based computational framework MAN-SDNE to predict any intermolecular associations. More specifically, we constructed a large-scale molecular association network of multiple biomolecules in human by integrating associations among long non-coding RNA, microRNA, protein, drug, and disease, containing 6,528 molecular nodes, 9 kind of,105,546 associations. And then, the feature of each node is represented by its network proximity and attribute features. Furthermore, these features are used to train Random Forest classifier to predict intermolecular associations. MAN-SDNE achieves a remarkable performance with an AUC of 0.9552 and an AUPR of 0.9338 under five-fold cross-validation. To indicate the ability to predict specific types of interactions, a case study for predicting lncRNA-protein interactions using MAN-SDNE is also executed. Experimental results demonstrate this work offers a systematic insight for understanding the synergistic associations between molecules and complex diseases and provides a network-based computational tool to systematically explore intermolecular interactions.

A survey of circular RNAs in complex diseases: databases, tools and computational methods

Circular RNAs (circRNAs) are a category of novelty discovered competing endogenous non-coding RNAs that have been proved to implicate many human complex diseases. A large number of circRNAs have been confirmed to be involved in cancer progression and are expected to become promising biomarkers for tumor diagnosis and targeted therapy. Deciphering the underlying relationships between circRNAs and diseases may provide new insights for us to understand the pathogenesis of complex diseases and further characterize the biological functions of circRNAs. As traditional experimental methods are usually time-consuming and laborious, computational models have made significant progress in systematically exploring potential circRNA-disease associations, which not only creates new opportunities for investigating pathogenic mechanisms at the level of circRNAs, but also helps to significantly improve the efficiency of clinical trials. In this review, we first summarize the functions and characteristics of circRNAs and introduce some representative circRNAs related to tumorigenesis. Then, we mainly investigate the available databases and tools dedicated to circRNA and disease studies. Next, we present a comprehensive review of computational methods for predicting circRNA-disease associations and classify them into five categories, including network propagating-based, path-based, matrix factorization-based, deep learning-based and other machine learning methods. Finally, we further discuss the challenges and future researches in this field.

GreenCircRNA: a database for plant circRNAs that act as miRNA decoys

Circular RNAs (circRNAs) are endogenous non-coding RNAs that form a covalently closed continuous loop, are widely distributed and play important roles in a series of developmental processes. In plants, an increasing number of studies have found that circRNAs can regulate plant metabolism and are involved in plant responses to biotic or abiotic stress. Acting as miRNA decoys is a critical way for circRNAs to perform their functions. Therefore, we developed GreenCircRNA—a database for plant circRNAs acting as miRNA decoys that is dedicated to providing a plant-based platform for detailed exploration of plant circRNAs and their potential decoy functions. This database includes over 210 000 circRNAs from 69 species of plants; the main data sources of circRNAs in this database are NCBI, EMBL-EBI and Phytozome. To investigate the function of circRNAs as competitive endogenous RNAs, the possibility of circRNAs from 38 plants to act as miRNA decoys was predicted. Moreover, we provide basic information for the circRNAs in the database, including their locations, host genes and relative expression levels, as well as full-length sequences, host gene GO (Gene Ontology) numbers and circRNA visualization. GreenCircRNA is the first database for the prediction of circRNAs that act as miRNA decoys and contains the largest number of plant species.

Database URL: http://greencirc.cn

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.

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.

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.

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.

The interplay between microRNA and alternative splicing of linear and circular RNAs in eleven plant species

MOTIVATION

MicroRNA (miRNA) and alternative splicing (AS)-mediated post-transcriptional regulation has been extensively studied in most eukaryotes. However, the interplay between AS and miRNAs has not been explored in plants. To our knowledge, the overall profile of miRNA target sites in circular RNAs (circRNA) generated by alternative back splicing has never been reported previously. To address the challenge, we identified miRNA target sites located in alternatively spliced regions of the linear and circular splice isoforms using the up-to-date single-molecule real-time (SMRT) isoform sequencing (Iso-Seq) and Illumina sequencing data in eleven plant species.

RESULTS

In total, we identified 399 401 and 114 574 AS events from linear and circular RNAs, respectively. Among them, there were 64 781 and 41 146 miRNA target sites located in linear and circular AS region, respectively. In addition, we found 38 913 circRNAs to be overlapping with 45 648 AS events of its own parent isoforms, suggesting circRNA regulation of AS of linear RNAs by forming R-loop with the genomic locus. Here, we present a comprehensive database of miRNA targets in alternatively spliced linear and circRNAs (ASmiR) and a web server for deposition and identification of miRNA target sites located in the alternatively spliced region of linear and circular RNAs. This database is accompanied by an easy-to-use web query interface for meaningful downstream analysis. Plant research community can submit user-defined datasets to the web service to search AS regions harboring small RNA target sites. In conclusion, this study provides an unprecedented resource to understand regulatory relationships between miRNAs and AS in both gymnosperms and angiosperms.

AVAILABILITY AND IMPLEMENTATION

The readily accessible database and web-based tools are available at http://forestry.fafu.edu.cn/bioinfor/db/ASmiR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Learning Representations to Predict Intermolecular Interactions on Large-Scale Heterogeneous Molecular Association Network

Molecular components that are functionally interdependent in human cells constitute molecular association networks. Disease can be caused by disturbance of multiple molecular interactions. New biomolecular regulatory mechanisms can be revealed by discovering new biomolecular interactions. To this end, a heterogeneous molecular association network is formed by systematically integrating comprehensive associations between miRNAs, lncRNAs, circRNAs, mRNAs, proteins, drugs, microbes, and complex diseases. We propose a machine learning method for predicting intermolecular interactions, named MMI-Pred. More specifically, a network embedding model is developed to fully exploit the network behavior of biomolecules, and attribute features are also calculated. Then, these discriminative features are combined to train a random forest classifier to predict intermolecular interactions. MMI-Pred achieves an outstanding performance of 93.50% accuracy in hybrid associations prediction under 5-fold cross-validation. This work provides systematic landscape and machine learning method to model and infer complex associations between various biological components.

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.

HIPK3 Promotes Growth and Metastasis of Esophageal Squamous Cell Carcinoma via Regulation of miR-599/c-MYC Axis

Background/Aims

this experimental design was based on HIPK3 to explore the pathogenesis of ESCC.

Methods

RT-qPCR was used to detect the expression of CircHIPK3 and miR-599 in ESCC tissues and cell lines.CCK-8, colony formation, flow cytometry and transwell assay were used to detect the effects of CircHIPK3 and miR-599 on tumor cell proliferation, apoptosis and migration and invasion. Target gene prediction and screening, luciferase reporter assays were used to validate downstream target genes of CircHIPK3 and miR-599.mRNA and protein expression of c-MYC were detected by RT-qPCR and Western blotting. The tumor changes in mice were detected by in vivo experiments in nude mice.

Results

HIPK3 was highly expressed in ESCC tissues and cell lines. In addition, HIPK3 expression levels were associated with advanced TNM stage, lymph node metastasis and tumor size. Moreover, HIPK3 was significantly promoted cell proliferation and migration of ESCC cells. In addition, HIPK3 was able to inhibit miRNA-599 expression and up-regulate the expression level of c-MYC. Finally, the results of in vivo animal models confirmed that HIPK3 promoted ESCC progression by modulating the miR-599/c-MYC axis.

Conclusion

HIPK3 can regulate the proliferation of esophageal squamous cell carcinoma cells by regulating miR-599/c-MYC axis, thereby inhibiting the occurrence and development of esophageal squamous cell carcinoma.

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.

Cereal Circular RNAs (circRNAs): An Overview of the Computational Resources for Identification and Analysis

Circular RNAs (circRNAs) are a widespread class of endogenous noncoding RNAs and they have been studied in the past few years, implying important biological functions in all kingdoms of life. Recently, circRNAs have been identified in many plant species, including cereal crops, showing differential expression during stress response and developmental programs, which suggests their role in these process. In the following years, it is expected that insights into the functional roles of circRNAs can be used by cereal scientists and molecular breeders with the aim to develop new strategies for crop improvement. Here, we briefly outline the current knowledge about circRNAs in plants and we also outline available computational resources for their validation and analysis in cereal species.

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

CropCircDB: a comprehensive circular RNA resource for crops in response to abiotic stress

Circular RNA (circRNAs) may mediate mRNA expression as miRNA sponge. Since the community has paid more attention on circRNAs, a lot of circRNA databases have been developed for plant. However, a comprehensive collection of circRNAs in crop response to abiotic stress is still lacking. In this work, we applied a big-data approach to take full advantage of large-scale sequencing data, and developed a rich circRNA resource: CropCircDB for maize and rice, later extending to incorporate more crop species. We also designed a metric: stress detections score, which is specifically for detecting circRNAs under stress condition. In summary, we systematically investigated 244 and 288 RNA-Seq samples for maize and rice, respectively, and found 38 785 circRNAs in maize, and 63 048 circRNAs in rice. This resource not only supports user-friendly JBrowser to visualize genome easily, but also provides elegant view of circRNA structures and dynamic profiles of circRNA expression in all samples. Together, this database will host all predicted and validated crop circRNAs response to abiotic stress.

CircFunBase: a database for functional circular RNAs

Increasing evidence reveals that circular RNAs (circRNAs) are widespread in eukaryotes and play important roles in diverse biological processes. However, a comprehensive functionally annotated circRNA database is still lacking. CircFunBase is a web-accessible database that aims to provide a high-quality functional circRNA resource including experimentally validated and computationally predicted functions. The current version of CircFunBase documents more than 7000 manually curated functional circRNA entries, mainly including Homo sapiens, Mus musculus etc. CircFunBase provides visualized circRNA-miRNA interaction networks. In addition, a genome browser is provided to visualize the genome context of circRNAs. As a biological information platform for circRNAs, CircFunBase will contribute for circRNA studies and bridge the gap between circRNAs and their functions.

DWNN-RLS: regularized least squares method for predicting circRNA-disease associations

BACKGROUND

Many evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. More importantly, circRNAs are also involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. Therefore, identifying the circRNA-disease associations is very appealing to comprehensively understand the mechanism, treatment and diagnose of diseases, yet challenging. As the complex mechanism between circRNAs and diseases, wet-lab experiments are expensive and time-consuming to discover novel circRNA-disease associations. Therefore, it is of dire need to employ the computational methods to discover novel circRNA-disease associations.

RESULT

In this study, we develop a method (DWNN-RLS) to predict circRNA-disease associations based on Regularized Least Squares of Kronecker product kernel. The similarity of circRNAs is computed from the Gaussian Interaction Profile(GIP) based on known circRNA-disease associations. In addition, the similarity of diseases is integrated by the mean of GIP similarity and sematic similarity which is computed by the direct acyclic graph (DAG) representation of diseases. The kernels of circRNA-disease pairs are constructed from the Kronecker product of the kernels of circRNAs and diseases. DWNN (decreasing weight k-nearest neighbor) method is adopted to calculate the initial relational score for new circRNAs and diseases. The Kronecker product kernel based regularised least squares approach is used to predict new circRNA-disease associations. We adopt 5-fold cross validation (5CV), 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) to assess the prediction performance of our method, and compare it with other six competing methods (RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP).

CONLUSION

The experiment results show that DWNN-RLS reaches the AUC values of 0.8854, 0.9205 and 0.9701 in 5CV, 10CV and LOOCV, respectively, which illustrates that DWNN-RLS is superior to the competing methods RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP. In addition, case studies also show that DWNN-RLS is an effective method to predict new circRNA-disease associations.

CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases

CircR2Disease is a manually curated database, which provides a comprehensive resource for circRNA deregulation in various diseases. Increasing evidences have shown that circRNAs play critical roles in transcriptional, post-transcriptional and translational regulation. Therefore, the aberrant expression of circRNAs has been associated with a group of diseases. It is significant to develop a high-quality database to deposit the deregulated circRNAs in diseases. The current version of CircR2Disease contains 725 associations between 661 circRNAs and 100 diseases by reviewing existing literatures. Each entry in the CircR2Disease contains detailed information for the circRNA–disease relationship, including circRNA name, coordinates and gene symbol, disease name, expression patterns of circRNA, experimental techniques, a brief description of the circRNA–disease relationship, year of publication and the PubMed ID. CircR2Disease provides a user-friendly interface to browse, search and download as well as to submit novel disease-related circRNAs. CircR2Disease could be very beneficial for researches to investigate the mechanism of disease-related circRNAs and explore the appropriate algorithms for predicting novel associations.

Database URL: http://bioinfo.snnu.edu.cn/CircR2Disease/

A critical and speculative review on microRNA technology in crop improvement: Current challenges and future directions

MicroRNAs (miRNAs) lie at the center of gene regulation and, as such, have become novel targets for crop improvement including the enhancement of crop quality and yields as well as responses to environmental stresses. There are several major issues related to miRNA technology including the functional analysis of miRNAs and their nomenclature. In this critical and speculative review, we recommend several directions for future plant miRNA research and perspectives. Research on miRNA needs to be extended from merely descriptive studies to functional studies. More genetic tools, such as genome editing, should be developed for miRNA functional study. Obtaining transgenic plants is a bottleneck for plant miRNA functional studies and, hence, more reliable transformation methods need to be developed. We also propose a new terminology approach for miRNA nomenclature. The current miRNA nomenclature is confusing and has mislead much research. Here we suggest to name a miRNA as miR#-5p or -3p, and to name their opposite strand as miR#*-3p or -5p. The advantages of the new nomenclature is that it covers information on the history, relationship, family, and location of an individual miRNA. It recognizes both traditional and new discovery.