PTESFinder: a computational method to identify post-transcriptional exon shuffling (PTES) events

Circular RNA discovery with emerging sequencing and deep learning technologies

Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disease pathogenesis. However, their low expression levels and high sequence similarity to linear RNAs present substantial challenges for circRNA detection and characterization. Recent advances in long-read and single-cell RNA sequencing technologies, coupled with sophisticated deep learning-based algorithms, have revolutionized the investigation of circRNAs at unprecedented resolution and scale. This Review summarizes recent breakthroughs in circRNA discovery, characterization and functional analysis algorithms. We also discuss the challenges associated with integrating large-scale circRNA sequencing data and explore the potential future development of artificial intelligence (AI)-driven algorithms to unlock the full potential of circRNA research in biomedical applications.

circ2LO: Identification of CircRNA Based on the LucaOne Large Model

Circular RNA is a type of noncoding RNA with a special covalent bond structure. As an endogenous RNA in animals and plants, it is formed through RNA splicing. The 5' and 3' ends of the exons form circular RNA at the back-splicing sites. Circular RNA plays an important regulatory role in diseases by interacting with the associated miRNAs. Accurate identification of circular RNA can enrich the data on circular RNA and provide new ideas for drug development. At present, mainstream circular RNA recognition algorithms are divided into two categories: those based on RNA sequence position information and those based on RNA sequence biometric information. Herein, we propose a method for the recognition of circular RNA, called circ2LO, which utilizes the LucaOne large model for feature embedding of the splicing sites of RNA sequences as well as their upstream and downstream sequences to prevent semantic information loss caused by the traditional one-hot encoding method. Subsequently, it employs a convolutional layer to extract features and a self-attention mechanism to extract interactive features to accurately capture the core features of the circular RNA at the splicing sites. Finally, it uses a fully connected layer to identify circular RNA. The accuracy of circ2LO on the human dataset reached 95.47%, which is higher than the values shown by existing methods. It also achieved accuracies of 97.04% and 72.04% on the Arabidopsis and mouse datasets, respectively, demonstrating good robustness. Through rigorous validation, the circ2LO model has proven its high-precision identification capability for circular RNAs, marking it as a potentially transformative analytical platform in the circRNA research field.

Deconvolution of the tumor-educated platelet transcriptome reveals activated platelet and inflammatory cell transcript signatures

Tumor-educated platelets (TEPs) are a potential method of liquid biopsy for the diagnosis and monitoring of cancer. However, the mechanism underlying tumor education of platelets is not known, and transcripts associated with TEPs are often not tumor-associated transcripts. We demonstrated that direct tumor transfer of transcripts to circulating platelets is an unlikely source of the TEP signal. We used CDSeq, a latent Dirichlet allocation algorithm, to deconvolute the TEP signal in blood samples from patients with glioblastoma. We demonstrated that a substantial proportion of transcripts in the platelet transcriptome are derived from nonplatelet cells, and the use of this algorithm allows the removal of contaminant transcripts. Furthermore, we used the results of this algorithm to demonstrate that TEPs represent a subset of more activated platelets, which also contain transcripts normally associated with nonplatelet inflammatory cells, suggesting that these inflammatory cells, possibly in the tumor microenvironment, transfer transcripts to platelets that are then found in circulation. Our analysis suggests a useful and efficient method of processing TEP transcriptomic data to enable the isolation of a unique TEP signal associated with specific tumors.

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.

Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression

Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.

Computational approaches for circRNAs prediction and in silico characterization

Circular RNAs (circRNAs) are single-stranded and covalently closed non-coding RNA molecules originated from RNA splicing. Their functions include regulatory potential over other RNA species, such as microRNAs, messenger RNAs and RNA binding proteins. For circRNA identification, several algorithms are available and can be classified in two major types: pseudo-reference-based and split-alignment-based approaches. In general, the data generated from circRNA transcriptome initiatives is deposited on public specific databases, which provide a large amount of information on different species and functional annotations. In this review, we describe the main computational resources for the identification and characterization of circRNAs, covering the algorithms and predictive tools to evaluate its potential role in a particular transcriptomics project, including the public repositories containing relevant data and information for circRNAs, recapitulating their characteristics, reliability and amount of data reported.

Advances in detecting N6-methyladenosine modification in circRNAs

Circular RNAs (circRNAs) are a class of noncoding RNAs with covalently single-stranded closed loop structures derived from back-splicing event of linear precursor mRNAs (pre-mRNAs). N6-methyladenosine (m⁶A), the most abundant epigenetic modification in eukaryotic RNAs, has been shown to play a crucial role in regulating the fate and biological function of circRNAs, and thus affecting various physiological and pathological processes. Accurate identification of m⁶A modification in circRNAs is an essential step to fully elucidate the crosstalk between m⁶A and circRNAs. In recent years, the rapid development of high-throughput sequencing technology and bioinformatic methodology has propelled the establishment of a multitude of approaches to detect circRNAs and m⁶A modification, including in vitro-based and in silico methods. Based on this, the research community has started on a new journey to develop methods for identification of m⁶A modification in circRNAs. In this review, we provide a comprehensive review and evaluation of the existing methods responsible for detecting circRNAs, m⁶A modification, and especially, m⁶A modification in circRNAs, which mainly focused on those developed based on high-throughput technologies and methodology of bioinformatics. This handy reference can help researchers figure out towards which direction this field will go.

Comprehensive comparison of two types of algorithm for circRNA detection from short-read RNA-Seq

MOTIVATION

Circular RNA is generally formed by the "back-splicing" process between the upstream splice acceptor and the downstream donor in/not in the regulation of the corresponding RNA-binding proteins or cis-elements. Therefore, more and more software packages have been developed and they are mostly based on the identification of the back-spliced junction reads. However, recent studies developed two software tools that can detect circRNA candidates by constructing k-mer table or/and de bruijn graph rather than reads mapping.

RESULTS

Here, we compared the precision, sensitivity and detection efficiency between software tools based on different algorithms. Eleven representative detection tools with two types of algorithm were selected for the overall pipeline analysis of RNA-seq datasets with/without RNase R treatment in two cell lines. Precision, sensitivity, AUC, F1 score and detection efficiency metrics were assessed to compare the prediction tools. Meanwhile, the sensitivity and distribution of highly expressed circRNAs before and after RNase R treatment were also revealed by their enrichment, unaffected and depleted candidate frequencies. Eventually, we found that compared to the k-mer based tools, CIRI2 and KNIFE based on reads mapping had relatively superior and more balanced detection performance regardless of the cell line or RNase R (-/+) datasets.

AVAILABILITY AND IMPLEMENTATION

All predicted results and source codes can be retrieved from https://github.com/luffy563/circRNA_tools_comparison.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Impact of high platelet turnover on the platelet transcriptome: Results from platelet RNA-sequencing in patients with sepsis

Background

Sepsis is associated with high platelet turnover and elevated levels of immature platelets. Changes in the platelet transcriptome and the specific impact of immature platelets on the platelet transcriptome remain unclear. Thus, this study sought to address whether and how elevated levels of immature platelets affect the platelet transcriptome in patients with sepsis.

Methods

Blood samples were obtained from patients with sepsis requiring vasopressor therapy (n = 8) and from a control group of patients with stable coronary artery disease and otherwise similar demographic characteristics (n = 8). Immature platelet fraction (IPF) was determined on a Sysmex XE 2100 analyser and platelet function was tested by impedance aggregometry. RNA from leukocyte-depleted platelets was used for transcriptome analysis by Next Generation Sequencing integrating the use of unique molecular identifiers.

Results

IPF (median [interquartile range]) was significantly elevated in sepsis patients (6.4 [5.3–8.7] % vs. 3.6 [2.6–4.6] %, p = 0.005). Platelet function testing revealed no differences in adenosine diphosphate- or thrombin receptor activating peptide-induced platelet aggregation between control and sepsis patients. Putative circular RNA transcripts were decreased in platelets from septic patients. Leukocyte contamination defined by CD45 abundance levels in RNA-sequencing was absent in both groups. Principal component analysis of transcripts showed only partial overlap of clustering with IPF levels. RNA sequencing showed up-regulation of 524 and down-regulation of 118 genes in platelets from sepsis patients compared to controls. Upregulated genes were mostly related to catabolic processes and protein translation. Comparison to published platelet transcriptomes showed a large overlap of changes observed in sepsis and COVID-19 but not with reticulated platelets from healthy donors.

Conclusions

Patients with sepsis appear to have a less degraded platelet transcriptome as indicated by increased levels of immature platelets and decreased levels of putative circular RNA transcripts. The present data suggests that increased protein translation is a characteristic mechanism of systemic inflammation.

Bioinformatic Analysis of CircRNA from RNA-seq Datasets

Circular RNAs (circRNAs) are a vast class of covalently closed, noncoding RNAs expressed in specific tissues and developmental stages. The molecular, cellular, and pathophysiologic roles of circRNAs are not fully known, but their impact on gene expression programs is beginning to emerge, as circRNAs often associate with RNA-binding proteins and nucleic acids. With rising interest in identifying circRNAs associated with disease processes, it has become particularly important to identify circRNAs in RNA sequencing (RNA-seq) datasets, either generated by the investigator or reported in the literature. Here, we present a methodology to identify and analyze circRNAs in RNA-seq datasets, including those archived in repositories. We elaborate on the unique features of circRNAs that require specialized attention in RNA-seq datasets, the software packages designed for circRNA identification, the ongoing efforts to reconstruct the body of circRNAs starting from unique circularizing junctions, and the interacting factors that can be proposed from putative circRNA body sequences. We discuss the advantages and limitations of the current approaches for high-throughput circRNA analysis from RNA-sequencing datasets and identify areas that would benefit from the development of superior bioinformatic tools.

Quantifying full-length circular RNAs in cancer

Circular RNAs (circRNAs) are abundantly expressed in cancer. Their resistance to exonucleases enables them to have potentially stable interactions with different types of biomolecules. Alternative splicing can create different circRNA isoforms that have different sequences and unequal interaction potentials. The study of circRNA function thus requires knowledge of complete circRNA sequences. Here we describe psirc, a method that can identify full-length circRNA isoforms and quantify their expression levels from RNA sequencing data. We confirm the effectiveness and computational efficiency of psirc using both simulated and actual experimental data. Applying psirc on transcriptome profiles from nasopharyngeal carcinoma and normal nasopharynx samples, we discover and validate circRNA isoforms differentially expressed between the two groups. Compared with the assumed circular isoforms derived from linear transcript annotations, some of the alternatively spliced circular isoforms have 100 times higher expression and contain substantially fewer microRNA response elements, showing the importance of quantifying full-length circRNA isoforms.

Genome-Wide RNA-Sequencing Reveals Massive Circular RNA Expression Changes of the Neurotransmission Genes in the Rat Brain after Ischemia-Reperfusion

Ischemic brain stroke is one of the most serious and socially significant diseases. In addition to messenger RNAs (mRNAs), encoding protein, the study of regulatory RNAs in ischemic has exceptional importance for the development of new strategies for neuroprotection. Circular RNAs (circRNAs) have a closed structure, predominantly brain-specific expression, and remain highly promising targets of research. They can interact with microRNAs (miRNAs), diminish their activity and thereby inhibit miRNA-mediated repression of mRNA. Genome-wide RNA-Seq analysis of the subcortical structures of the rat brain containing an ischemic damage focus and penumbra area revealed 395 circRNAs changed their expression significantly at 24 h after transient middle cerebral artery occlusion model (tMCAO) conditions. Furthermore, functional annotation revealed their association with neuroactive signaling pathways. It was found that about a third of the differentially expressed circRNAs (DECs) originate from genes whose mRNA levels also changed at 24 h after tMCAO. The other DECs originate from genes encoding non-regulated mRNAs under tMCAO conditions. In addition, bioinformatic analysis predicted a circRNA–miRNA–mRNA network which was associated with the neurotransmission signaling regulation. Our results show that such circRNAs can persist as potential miRNA sponges for the protection of mRNAs of neurotransmitter genes. The results expanded our views about the neurotransmission regulation in the rat brain after ischemia–reperfusion with circRNA action.

A genome-wide circular RNA transcriptome in rat

Circular RNAs (circRNAs) are a novel class of noncoding RNAs that back-splice from 5ʹ donor site and 3ʹ acceptor sites to form a circular structure. A number of circRNAs have been discovered in model organisms including human, mouse, Drosophila, among other organisms. There are a few candidate-based studies on circRNAs in rat, a well-studied model organism as well. A number of pipelines have been published to identify the back splice junctions for the discovery of circRNAs but studies comparing these tools have suggested that a combination of tools would be a better approach to identify high-confidence circRNAs. The availability of a recent dataset of transcriptomes encompassing 11 tissues, 4 developmental stages, and 2 genders motivated us to explore the landscape of circRNAs in the organism in this context. In order to understand the difference among different pipelines, we employed five different combinations of tools to identify circular RNAs from the dataset. We compared the results of the different combination of tools/pipelines with respect to alignment, total number of circRNAs identified and read-coverage. In addition, we identified tissue-specific, development-stage specific and gender-specific circRNAs and further independently validated 16 circRNA junctions out of 24 selected candidates in 5 tissue samples and estimated the quantitative expression of five circRNA candidates using real-time polymerase chain reaction and our analysis suggests three candidates as tissue-enriched. This study is one of the most comprehensive studies which provides a map of circRNAs transcriptome as well as to understand the difference among different computational pipelines in rat.

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.

Role of Circular RNA in Kidney-Related Diseases

The kidney is vital in maintaining fluid, electrolyte, and acid–base balance. Kidney-related diseases, which are an increasing public health issue, can happen to people of any age and at any time. Circular RNAs (circRNAs) are endogenous RNA that are produced by selective RNA splicing and are involved in progression of various diseases. Studies have shown that various kidney diseases, including renal cell carcinoma, acute kidney injury, and chronic kidney disease, are linked to circRNAs. This review outlines the characteristics and biological functions of circRNAs and discusses specific studies that provide insights into the function and potential of circRNAs for application in the diagnosis and treatment of kidney-related diseases.

Insights Into the Role of CircRNAs: Biogenesis, Characterization, Functional, and Clinical Impact in Human Malignancies

Circular RNAs (circRNAs) are an evolutionarily conserved novel class of non-coding endogenous RNAs (ncRNAs) found in the eukaryotic transcriptome, originally believed to be aberrant RNA splicing by-products with decreased functionality. However, recent advances in high-throughput genomic technology have allowed circRNAs to be characterized in detail and revealed their role in controlling various biological and molecular processes, the most essential being gene regulation. Because of the structural stability, high expression, availability of microRNA (miRNA) binding sites and tissue-specific expression, circRNAs have become hot topic of research in RNA biology. Compared to the linear RNA, circRNAs are produced differentially by backsplicing exons or lariat introns from a pre-messenger RNA (mRNA) forming a covalently closed loop structure missing 3' poly-(A) tail or 5' cap, rendering them immune to exonuclease-mediated degradation. Emerging research has identified multifaceted roles of circRNAs as miRNA and RNA binding protein (RBP) sponges and transcription, translation, and splicing event regulators. CircRNAs have been involved in many human illnesses, including cancer and neurodegenerative disorders such as Alzheimer's and Parkinson's disease, due to their aberrant expression in different pathological conditions. The functional versatility exhibited by circRNAs enables them to serve as potential diagnostic or predictive biomarkers for various diseases. This review discusses the properties, characterization, profiling, and the diverse molecular mechanisms of circRNAs and their use as potential therapeutic targets in different human malignancies.

Roles for RNA export factor, Nxt1, in ensuring muscle integrity and normal RNA expression in Drosophila

The mRNA export pathway is responsible for the transport of mRNAs from the nucleus to the cytoplasm, and thus is essential for protein production and normal cellular functions. A partial loss of function allele of the mRNA export factor Nxt1 in Drosophila shows reduced viability and sterility. A previous study has shown that the male fertility defect is due to a defect in transcription and RNA stability, indicating the potential for this pathway to be implicated in processes beyond the known mRNA transport function. Here we investigate the reduced viability of Nxt1 partial loss of function mutants, and describe a defect in growth and maintenance of the larval muscles, leading to muscle degeneration. RNA-seq revealed reduced expression of a set of mRNAs, particularly from genes with long introns in Nxt1 mutant carcass. We detected differential expression of circRNA, and significantly fewer distinct circRNAs expressed in the mutants. Despite the widespread defects in gene expression, muscle degeneration was rescued by increased expression of the costamere component tn (abba) in muscles. This is the first report of a role for the RNA export pathway gene Nxt1 in the maintenance of muscle integrity. Our data also links the mRNA export pathway to a specific role in the expression of mRNA and circRNA from common precursor genes, in vivo.

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.

Transposable elements, circular RNAs and mitochondrial transcription in age-related genomic regulation

Our understanding of the molecular regulation of aging and age-related diseases is still in its infancy, requiring in-depth characterization of the molecular landscape shaping these complex phenotypes. Emerging classes of molecules with promise as aging modulators include transposable elements, circRNAs and the mitochondrial transcriptome. Analytical complexity means that these molecules are often overlooked, even though they exhibit strong associations with aging and, in some cases, may directly contribute to its progress. Here, we review the links between these novel factors and age-related phenotypes, and we suggest tools that can be easily incorporated into existing pipelines to better understand the aging process.

Islet-expressed circular RNAs are associated with type 2 diabetes status in human primary islets and in peripheral blood

BACKGROUND

Circular RNAs are non-coding RNA molecules with gene regulatory potential that have been associated with several human diseases. They are stable and present in the circulation, making them excellent candidates for biomarkers of disease. Despite their promise as biomarkers or future therapeutic targets, information on their expression and functionality in human pancreatic islets is a relatively unexplored subject.

METHODS

Here we aimed to produce an enriched circRNAome profile for human pancreatic islets by CircleSeq, and to explore the relationship between circRNA expression, diabetes status, genotype at T2D risk loci and measures of glycaemia (insulin secretory index; SI and HbA1c) in human islet preparations from healthy control donors and donors with type 2 diabetes using ANOVA or linear regression as appropriate. We also assessed the effect of elevated glucose, cytokine and lipid and hypoxia on circRNA expression in the human beta cell line EndoC-βH1.

RESULTS

We identified over 2600 circRNAs present in human islets. Of the five most abundant circRNAs in human islets, four (circCIRBP, circZKSCAN, circRPH3AL and circCAMSAP1) demonstrated marked associations with diabetes status. CircCIRBP demonstrated an association with insulin secretory index in isolated human islets and circCIRBP and circRPH3AL displayed altered expression with elevated fatty acid in treated EndoC-βH1 cells. CircCAMSAP1 was also noted to be associated with T2D status in human peripheral blood. No associations between circRNA expression and genotype at T2D risk loci were identified in our samples.

CONCLUSIONS

Our data suggest that circRNAs are abundantly expressed in human islets, and that some are differentially regulated in the islets of donors with type 2 diabetes. Some islet circRNAs are also expressed in peripheral blood and the expression of one, circCAMSAP1, correlates with diabetes status. These findings highlight the potential of circRNAs as biomarkers for T2D.

CircMiner: accurate and rapid detection of circular RNA through splice-aware pseudo-alignment scheme

Motivation

The ubiquitous abundance of circular RNAs (circRNAs) has been revealed by performing high-throughput sequencing in a variety of eukaryotes. circRNAs are related to some diseases, such as cancer in which they act as oncogenes or tumor-suppressors and, therefore, have the potential to be used as biomarkers or therapeutic targets. Accurate and rapid detection of circRNAs from short reads remains computationally challenging. This is due to the fact that identifying chimeric reads, which is essential for finding back-splice junctions, is a complex process. The sensitivity of discovery methods, to a high degree, relies on the underlying mapper that is used for finding chimeric reads. Furthermore, all the available circRNA discovery pipelines are resource intensive.

Results

We introduce CircMiner, a novel stand-alone circRNA detection method that rapidly identifies and filters out linear RNA sequencing reads and detects back-splice junctions. CircMiner employs a rapid pseudo-alignment technique to identify linear reads that originate from transcripts, genes or the genome. CircMiner further processes the remaining reads to identify the back-splice junctions and detect circRNAs with single-nucleotide resolution. We evaluated the efficacy of CircMiner using simulated datasets generated from known back-splice junctions and showed that CircMiner has superior accuracy and speed compared to the existing circRNA detection tools. Additionally, on two RNase R treated cell line datasets, CircMiner was able to detect most of consistent, high confidence circRNAs compared to untreated samples of the same cell line.

Availability and implementation

CircMiner is implemented in C++ and is available online at https://github.com/vpc-ccg/circminer.

Supplementary information

Supplementary data are available at Bioinformatics online.

CircAST: Full-length Assembly and Quantification of Alternatively Spliced Isoforms in Circular RNAs

Circular RNAs (circRNAs), covalently closed continuous RNA loops, are generated from cognate linear RNAs through back splicing events, and alternative splicing events may generate different circRNA isoforms at the same locus. However, the challenges of reconstruction and quantification of alternatively spliced full-length circRNAs remain unresolved. On the basis of the internal structural characteristics of circRNAs, we developed CircAST, a tool to assemble alternatively spliced circRNA transcripts and estimate their expression by using multiple splice graphs. Simulation studies showed that CircAST correctly assembled the full sequences of circRNAs with a sensitivity of 85.63%–94.32% and a precision of 81.96%–87.55%. By assigning reads to specific isoforms, CircAST quantified the expression of circRNA isoforms with correlation coefficients of 0.85–0.99 between theoretical and estimated values. We evaluated CircAST on an in-house mouse testis RNA-seq dataset with RNase R treatment for enriching circRNAs and identified 380 circRNAs with full-length sequences different from those of their corresponding cognate linear RNAs. RT-PCR and Sanger sequencing analyses validated 32 out of 37 randomly selected isoforms, thus further indicating the good performance of CircAST, especially for isoforms with low abundance. We also applied CircAST to published experimental data and observed substantial diversity in circular transcripts across samples, thus suggesting that circRNA expression is highly regulated. CircAST can be accessed freely at https://github.com/xiaofengsong/CircAST.

circRNAs expressed in human peripheral blood are associated with human aging phenotypes, cellular senescence and mouse lifespan

Circular RNAs (circRNAs) are an emerging class of non-coding RNA molecules that are thought to regulate gene expression and human disease. Despite the observation that circRNAs are known to accumulate in older organisms and have been reported in cellular senescence, their role in aging remains relatively unexplored. Here, we have assessed circRNA expression in aging human blood and followed up age-associated circRNA in relation to human aging phenotypes, mammalian longevity as measured by mouse median strain lifespan and cellular senescence in four different primary human cell types. We found that circRNAs circDEF6, circEP300, circFOXO3 and circFNDC3B demonstrate associations with parental longevity or hand grip strength in 306 subjects from the InCHIANTI study of aging, and furthermore, circFOXO3 and circEP300 also demonstrate differential expression in one or more human senescent cell types. Finally, four circRNAs tested showed evidence of conservation in mouse. Expression levels of one of these, circPlekhm1, was nominally associated with lifespan. These data suggest that circRNA may represent a novel class of regulatory RNA involved in the determination of aging phenotypes, which may show future promise as both biomarkers and future therapeutic targets for age-related disease.

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.

An integrated transcriptional analysis of the developing human retina

The scarcity of embryonic/foetal material as a resource for direct study means that there is still limited understanding of human retina development. Here, we present an integrated transcriptome analysis combined with immunohistochemistry in human eye and retinal samples from 4 to 19 post-conception weeks. This analysis reveals three developmental windows with specific gene expression patterns that informed the sequential emergence of retinal cell types and enabled identification of stage-specific cellular and biological processes, and transcriptional regulators. Each stage is characterised by a specific set of alternatively spliced transcripts that code for proteins involved in the formation of the photoreceptor connecting cilium, pre-mRNA splicing and epigenetic modifiers. Importantly, our data show that the transition from foetal to adult retina is characterised by a large increase in the percentage of mutually exclusive exons that code for proteins involved in photoreceptor maintenance. The circular RNA population is also defined and shown to increase during retinal development. Collectively, these data increase our understanding of human retinal development and the pre-mRNA splicing process, and help to identify new candidate disease genes.

Methods for Annotation and Validation of Circular RNAs from RNAseq Data

Circular RNAs are an emerging class of transcript isoforms created by unique back splicing of exons to form a closed covalent circular structure. While initially considered as product of aberrant splicing, recent evidence suggests unique functions and conservation across evolution. While circular RNAs could be largely attributed to have little or no potential to encode for proteins, recent evidence points to at least a small subset of circular RNAs which encode for peptides. Circular RNAs are also increasingly shown to be biomarkers for a number of diseases including neurological disorders and cancer. The advent of deep sequencing has enabled large-scale identification of circular RNAs in human and other genomes. A number of computational approaches have come up in recent years to query circular RNAs on a genome-wide scale from RNA-seq data. In this chapter, we describe the application and methodology of identifying circular RNAs using three popular computational tools: FindCirc, Segemehl, and CIRI along with approaches for experimental validation of the unique splice junctions.

Maximizing the Utility of Cancer Transcriptomic Data

Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.

A 360° view of circular RNAs: From biogenesis to functions

The first circular RNA (circRNA) was identified more than 40 years ago, but it was only recently appreciated that circRNAs are common outputs of many eukaryotic protein-coding genes. Some circRNAs accumulate to higher levels than their associated linear mRNAs, especially in the nervous system, and have clear regulatory functions that result in organismal phenotypes. The pre-mRNA splicing machinery generates circRNAs via backsplicing reactions, which are often facilitated by intronic repeat sequences that base pair to one another and bring the intervening splice sites into close proximity. When spliceosomal components are limiting, circRNAs can become the preferred gene output, and backsplicing reactions are further controlled by exon skipping events and the combinatorial action of RNA binding proteins. This allows circRNAs to be expressed in a tissue- and stage-specific manner. Once generated, circRNAs are highly stable transcripts that often accumulate in the cytoplasm. The functions of most circRNAs remain unknown, but some can regulate the activities of microRNAs or be translated to produce proteins. Circular RNAs can further interface with the immune system as well as control gene expression events in the nucleus, including alternative splicing decisions. Circular RNAs thus represent a large class of RNA molecules that are tightly regulated, and it is becoming increasingly clear that they likely impact many biological processes. This article is categorized under: RNA Processing > Splicing Mechanisms RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution RNA Evolution and Genomics > Computational Analyses of RNA.

Molecular functions and specific roles of circRNAs in the cardiovascular system

As part of the superfamily of long noncoding RNAs, circular RNAs (circRNAs) are emerging as a new type of regulatory molecules that partake in gene expression control. Here, we review the current knowledge about circRNAs in cardiovascular disease. CircRNAs are not only associated with different types of cardiovascular disease, but they have also been identified as intracellular effector molecules for pathophysiological changes in cardiovascular tissues, and as cardiovascular biomarkers. This evidence is put in the context of the current understanding of general circRNA biogenesis and of known interactions of circRNAs with DNA, RNA, and proteins.

Analysis of human ES cell differentiation establishes that the dominant isoforms of the lncRNAs RMST and FIRRE are circular

Background

Circular RNAs (circRNAs) are predominantly derived from protein coding genes, and some can act as microRNA sponges or transcriptional regulators. Changes in circRNA levels have been identified during human development which may be functionally important, but lineage-specific analyses are currently lacking. To address this, we performed RNAseq analysis of human embryonic stem (ES) cells differentiated for 90 days towards 3D laminated retina.

Results

A transcriptome-wide increase in circRNA expression, size, and exon count was observed, with circRNA levels reaching a plateau by day 45. Parallel statistical analyses, controlling for sample and locus specific effects, identified 239 circRNAs with expression changes distinct from the transcriptome-wide pattern, but these all also increased in abundance over time. Surprisingly, circRNAs derived from long non-coding RNAs (lncRNAs) were found to account for a significantly larger proportion of transcripts from their loci of origin than circRNAs from coding genes. The most abundant, circRMST:E12-E6, showed a > 100X increase during differentiation accompanied by an isoform switch, and accounts for > 99% of RMST transcripts in many adult tissues. The second most abundant, circFIRRE:E10-E5, accounts for > 98% of FIRRE transcripts in differentiating human ES cells, and is one of 39 FIRRE circRNAs, many of which include multiple unannotated exons.

Conclusions

Our results suggest that during human ES cell differentiation, changes in circRNA levels are primarily globally controlled. They also suggest that RMST and FIRRE, genes with established roles in neurogenesis and topological organisation of chromosomal domains respectively, are processed as circular lncRNAs with only minor linear species.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4660-7) contains supplementary material, which is available to authorized users.

Circular RNAs in the cardiovascular system

Until recently considered as rare, circular RNAs (circRNAs) are emerging as important regulators of gene expression. They are ubiquitously expressed and represent a novel branch of the family of non-coding RNAs. Recent investigations showed that circRNAs are regulated in the cardiovascular system and participate in its physiological and pathological development. In this review article, we will provide an overview of the role of circRNAs in cardiovascular health and disease. After a description of the biogenesis of circRNAs, we will summarize what is known of the expression, regulation and function of circRNAs in the cardiovascular system. We will then address some technical aspects of circRNAs research, discussing how artificial intelligence may aid in circRNAs research. Finally, the potential of circRNAs as biomarkers of cardiovascular disease will be addressed and directions for future research will be proposed.

Computational Strategies for Exploring Circular RNAs

Recent studies have demonstrated that circular RNAs (circRNAs) are ubiquitous and have diverse functions and mechanisms of biogenesis. In these studies, computational profiling of circRNAs has been prevalently used as an indispensable method to provide high-throughput approaches to detect and analyze circRNAs. However, without an overall understanding of the underlying strategies, these computational methods may not be appropriately selected or used for a specific research purpose, and some misconceptions may result in biases in the analyses. In this review we attempt to illustrate the key steps and summarize tradeoff of different strategies, covering all popular algorithms for circRNA detection and various downstream analyses. We also clarify some common misconceptions and put emphasis on the fields of application for these computational methods.

Prospective Advances in Circular RNA Investigation

circRNAs have emerged as one of the key regulators in many cellular mechanisms and pathogenesis of diseases. However, with the limited knowledge and current technologies for circRNA investigations, there are several challenges that need to be addressed for. These include challenges in understanding the regulation of circRNA biogenesis, experimental designs, and sample preparations to characterize the circRNAs in diseases as well as the bioinformatics pipelines and algorithms. In this chapter, we discussed the above challenges and possible strategies to overcome those limitations. We also addressed the differences between the existing applications and technologies to study the circRNAs in diseases. By addressing these challenges, further understanding of circRNAs roles and regulations as well as the discovery of novel circRNAs could be achieved.

Circular RNAs: Biogenesis, Function, and a Role as Possible Cancer Biomarkers

Circular RNAs (circRNAs) are a class of noncoding RNAs (ncRNAs) that form covalently closed continuous loop structures, lacking the terminal 5' and 3' ends. CircRNAs are generated in the process of back-splicing and can originate from different genomic regions. Their unique circular structure makes circRNAs more stable than linear RNAs. In addition, they also display insensitivity to ribonuclease activity. Generally, circRNAs function as microRNA (miRNA) sponges and have a regulatory role in transcription and translation. They may be also translated in a cap-independent manner in vivo, to generate specific proteins. In the last decade, next-generation sequencing techniques, especially RNA-seq, have revealed great abundance and also dysregulation of many circRNAs in various diseases, suggesting their involvement in disease development and progression. Regarding their high stability and relatively specific differential expression patterns in tissues and extracellular environment (e.g., body fluids), they are regarded as promising novel biomarkers in cancer. Therefore, we focus this review on describing circRNA biogenesis, function, and involvement in human cancer development and address the potential of circRNAs to be effectively used as novel cancer diagnostic and prognostic biomarkers.

A comprehensive overview and evaluation of circular RNA detection tools

Circular RNA (circRNA) is mainly generated by the splice donor of a downstream exon joining to an upstream splice acceptor, a phenomenon known as backsplicing. It has been reported that circRNA can function as microRNA (miRNA) sponges, transcriptional regulators, or potential biomarkers. The availability of massive non-polyadenylated transcriptomes data has facilitated the genome-wide identification of thousands of circRNAs. Several circRNA detection tools or pipelines have recently been developed, and it is essential to provide useful guidelines on these pipelines for users, including a comprehensive and unbiased comparison. Here, we provide an improved and easy-to-use circRNA read simulator that can produce mimicking backsplicing reads supporting circRNAs deposited in CircBase. Moreover, we compared the performance of 11 circRNA detection tools on both simulated and real datasets. We assessed their performance regarding metrics such as precision, sensitivity, F1 score, and Area under Curve. It is concluded that no single method dominated on all of these metrics. Among all of the state-of-the-art tools, CIRI, CIRCexplorer, and KNIFE, which achieved better balanced performance between their precision and sensitivity, compared favorably to the other methods.

CirComPara: A Multi-Method Comparative Bioinformatics Pipeline to Detect and Study circRNAs from RNA-seq Data

Circular RNAs (circRNAs) are generated by back-splicing of immature RNA forming covalently closed loops of intron/exon RNA molecules. Pervasiveness, evolutionary conservation, massive and regulated expression, and post-transcriptional regulatory roles of circRNAs in eukaryotes have been appreciated and described only recently. Moreover, being easily detectable disease markers, circRNAs undoubtedly represent a molecular class with high bearing on molecular pathobiology. CircRNAs can be detected from RNA-seq data using appropriate computational methods to identify the sequence reads spanning back-splice junctions that do not co-linearly map to the reference genome. To this end, several programs were developed and critical assessment of various strategies and tools suggested the combination of at least two methods as good practice to guarantee robust circRNA detection. Here, we present CirComPara (http://github.com/egaffo/CirComPara), an automated bioinformatics pipeline, to detect, quantify and annotate circRNAs from RNA-seq data using in parallel four different methods for back-splice identification. CirComPara also provides quantification of linear RNAs and gene expression, ultimately comparing and correlating circRNA and gene/transcript expression levels. We applied our method to RNA-seq data of monocyte and macrophage samples in relation to haploinsufficiency of the RNA-binding splicing factor Quaking (QKI). The biological relevance of the results, in terms of number, types and variations of circRNAs expressed, illustrates CirComPara potential to enlarge the knowledge of the transcriptome, adding details on the circRNAome, and facilitating further computational and experimental studies.