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

Accurate assembly of circular RNAs with TERRACE

Circular RNA (circRNA) is a class of RNA molecules that forms a closed loop with their 5' and 3' ends covalently bonded. CircRNAs are known to be more stable than linear RNAs, have distinct properties and functions, and are promising biomarkers. Existing methods for assembling circRNAs heavily rely on the annotated transcriptomes, hence exhibiting unsatisfactory accuracy without a high-quality transcriptome. We present TERRACE, a new algorithm for full-length assembly of circRNAs from paired-end total RNA-seq data. TERRACE uses the splice graph as the underlying data structure that organizes the splicing and coverage information. We transform the problem of assembling circRNAs into finding paths that "bridge" the three fragments in the splice graph induced by back-spliced reads. We adopt a definition for optimal bridging paths and a dynamic programming algorithm to calculate such optimal paths. TERRACE features an efficient algorithm to detect back-spliced reads missed by RNA-seq aligners, contributing to its much-improved sensitivity. It also incorporates a new machine-learning approach trained to assign a confidence score to each assembled circRNA, which is shown to be superior to using abundance for scoring. On both simulations and biological data sets, TERRACE consistently outperforms existing methods by a large margin in sensitivity while achieving better or comparable precision. In particular, when the annotations are not provided, TERRACE assembles 123%-413% more correct circRNAs than state-of-the-art methods. TERRACE presents a significant advance in assembling full-length circRNAs from RNA-seq data, and we expect it to be widely used in future research on circRNAs.

Full-length circRNA sequencing method using low-input RNAs and profiling of circRNAs in MPTP-PD mice on a nanopore platform

Considering the importance of accurate information of full-length (FL) transcripts in functional analysis, researchers prefer to develop new sequencing methods based on third-generation sequencing (TGS) rather than short-read sequencing. Several FL circRNA sequencing strategies have been developed. However, the current methods are inapplicable to low-biomass samples, since a large amount of total RNAs are acquired for circRNA enrichment before library preparation. In this work, we developed an effective method to detect FL circRNAs from a nanogram level (1-100 ng) of total RNAs based on a nanopore platform. Additionally, prior to the library preparation process, we added a series of 24 nt barcodes for each sample to reduce the cost and operating time. Using this method, we profiled circRNA expression in the striatum, hippocampus and cerebral cortex of a Parkinson's disease (PD) mouse model. Over 6% of reads were effective for FL circRNA identification in most datasets. Notably, a reduction in the RNA initial input resulted in a lower correlation between replicates and the detection efficiency for longer circRNA, but the lowest input (1 ng) was able to detect numerous FL circRNAs. Next, we systematically identified over 263 934 circRNAs in PD and healthy mice using the lower-input FL sequencing method, some of which came from 50.52% of PD-associated genes. Moreover, significant changes were observed in the circRNA expression pattern at an isoform level, and high-confidence protein translation evidence was predicted. Overall, we developed an effective method to characterize FL circRNAs from low-input samples and provide a comprehensive insight into the biological function of circRNAs in PD at an isoform level.

Circular RNA expression in turkey skeletal muscle satellite cells is significantly altered by thermal challenge

Introduction

Understanding the genetic mechanisms behind muscle growth and development is crucial for improving the efficiency of animal protein production. Recent poultry studies have identified genes related to muscle development and explored how environmental stressors, such as temperature extremes, affect protein production and meat quality. Non-coding RNAs, including circular RNAs (circRNAs), play crucial roles in modulating gene expression and regulating the translation of mRNAs into proteins. This study examined circRNA expression in turkey skeletal muscle stem cells under thermal stress. The objectives were to identify and quantify circRNAs, assess circRNA abundance following RNAse R depletion, identify differentially expressed circRNAs (DECs), and predict potential microRNA (miRNA) targets for DECs and their associated genes.

Materials and methods

Cultured cells from two genetic lines (Nicholas commercial turkey and The Ohio State Random Bred Control 2) under three thermal treatments: cold (33°C), control (38°C), and hot (43°C) were compared at both the proliferation and differentiation stages. CircRNA prediction and differential expression and splicing analyses were conducted using the CIRIquant pipeline for both the untreated and RNase R depletion treated libraries. Predicted interactions between DECs and miRNAs, as well as the potential impact of circRNA secondary structure on these interactions, were investigated.

Results

A total of 11,125 circRNAs were predicted within the treatment groups, between both untreated and RNase R treated libraries. Differential expression analyses indicated that circRNA expression was significantly altered by thermal treatments and the genetic background of the stem cells. A total of 140 DECs were identified across the treatment comparisons. In general, more DECs within temperature treatment comparisons were identified in the proliferation stage and more DECs within genetic line comparisons were identified in the differentiation stage.

Discussion

This study highlights the significant impact of environmental stressors on non-coding RNAs and their role in gene regulation. Elucidating the role of non-coding RNAs in gene regulation can help further our understanding of muscle development and poultry production, underscoring the broader implications of this research for enhancing animal protein production efficiency.

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.

CircCamsap1 is dispensable for male fertility in mice

Background

Circular RNAs (circRNAs) are a large class of RNAs present in mammals. Among these, circCamsap1 is a well-acknowledged circRNA with significant implications, particularly in the development and progression of diverse tumors. However, the potential consequences of circCamsap1 depletion in vivo on male reproduction are yet to be thoroughly investigated.

Methods

The presence of circCamsap1 in the mouse testes was confirmed, and gene expression analysis was performed using reverse transcription quantitative polymerase chain reaction. CircCamsap1 knockout mice were generated utilizing the CRISPR/Cas9 system. Phenotypic analysis of both the testes and epididymis was conducted using histological and immunofluorescence staining. Additionally, fertility and sperm motility were assessed.

Results

Here, we successfully established a circCamsap1 knockout mouse model without affecting the expression of parental gene. Surprisingly, male mice lacking circCamsap1 (circCamsap1-/-) exhibited normal fertility, with no discernible differences in testicular and epididymal histology, spermatogenesis, sperm counts or sperm motility compared to circCamsap1+/+ mice. These findings suggest that circCamsap1 may not play an essential role in physiological spermatogenesis. Nonetheless, this result also underscores the complexity of circRNA function in male reproductive biology. Therefore, further research is necessary to elucidate the precise roles of other circRNAs in regulating male fertility.

Current advances in circular RNA detection and investigation methods: Are we running in circles?

Circular RNAs (circRNAs), characterized by their closed-loop structure, have emerged as significant transcriptomic regulators, with roles spanning from microRNA sponging to modulation of gene expression and potential peptide coding. The discovery and functional analysis of circRNAs have been propelled by advancements in both experimental and bioinformatics tools, yet the field grapples with challenges related to their detection, isoform diversity, and accurate quantification. This review navigates through the evolution of circRNA research methodologies, from early detection techniques to current state-of-the-art approaches that offer comprehensive insights into circRNA biology. We examine the limitations of existing methods, particularly the difficulty in differentiating circRNA isoforms and distinguishing circRNAs from their linear counterparts. A critical evaluation of various bioinformatics tools and novel experimental strategies is presented, emphasizing the need for integrated approaches to enhance our understanding and interpretation of circRNA functions. Our insights underscore the dynamic and rapidly advancing nature of circRNA research, highlighting the ongoing development of analytical frameworks designed to address the complexity of circRNAs and facilitate the assessment of their clinical utility. As such, this comprehensive overview aims to catalyze further advancements in circRNA study, fostering a deeper understanding of their roles in cellular processes and potential implications in disease. This article is categorized under: RNA Methods > RNA Nanotechnology RNA Methods > RNA Analyses in Cells RNA Methods > RNA Analyses In Vitro and In Silico.

Systematic identification and characterization of exon-intron circRNAs

Exon-intron circRNAs (EIciRNAs) are a circRNA subclass with retained introns. Global features of EIciRNAs remain largely unexplored, mainly owing to the lack of bioinformatic tools. The regulation of intron retention (IR) in EIciRNAs and the associated functionality also require further investigation. We developed a framework, FEICP, which efficiently detected EIciRNAs from high-throughput sequencing (HTS) data. EIciRNAs are distinct from exonic circRNAs (EcircRNAs) in aspects such as with larger length, localization in the nucleus, high tissue specificity, and enrichment mostly in the brain. Deep learning analyses revealed that compared with regular introns, the retained introns of circRNAs (CIRs) are shorter in length, have weaker splice site strength, and have higher GC content. Compared with retained introns in linear RNAs (LIRs), CIRs are more likely to form secondary structures and show greater sequence conservation. CIRs are closer to the 5'-end, whereas LIRs are closer to the 3'-end of transcripts. EIciRNA-generating genes are more actively transcribed and associated with epigenetic marks of gene activation. Computational analyses and genome-wide CRISPR screening revealed that SRSF1 binds to CIRs and inhibits the biogenesis of most EIciRNAs. SRSF1 regulates the biogenesis of EIciLIMK1, which enhances the expression of LIMK1 in cis to boost neuronal differentiation, exemplifying EIciRNA physiological function. Overall, our study has developed the FEICP pipeline to identify EIciRNAs from HTS data, and reveals multiple features of CIRs and EIciRNAs. SRSF1 has been identified to regulate EIciRNA biogenesis. EIciRNAs and the regulation of EIciRNA biogenesis play critical roles in neuronal differentiation.

Accurate Assembly of Circular RNAs with TERRACE

Circular RNA (circRNA) is a class of RNA molecules that forms a closed loop with its 5' and 3' ends covalently bonded. Due to this specific structure circRNAs are more stable than linear RNAs, admit distinct biological properties and functions, and have been proven to be promising biomarkers. Circular RNAs were severely overlooked previously owing to the biases in the RNA-seq protocols and in the detection algorithms, but recently gained tremendous attentions in both aspects. However, most existing methods for assembling circRNAs heavily rely on the annotated transcriptomes, and hence exhibit unsatisfactory accuracy when a high-quality transcriptome is unavailable. Here we present TERRACE, a new algorithm for full-length assembly of circRNAs from paired-end total RNA-seq data. TERRACE uses the splice graph as the underlying data structure to organize the splicing and coverage information. We transform the problem of assembling circRNAs into finding two paths that "bridge" the three fragments in the splice graph induced by back-spliced reads. To solve this formulation, we adopted a definition for optimal bridging paths and a dynamic programming algorithm to calculate such paths, an approach that was proven useful for assembling linear RNAs. TERRACE features an efficient algorithm to detect back-spliced reads that are missed by RNA-seq aligners, contributing to its much improved sensitivity. It also incorporates a new machine-learning approach that is trained to assign a confidence score to each assembled circRNA, which is shown superior to using abundance for scoring. TERRACE is compared with leading circRNA detection methods on both simulations and biological datasets. Our method consistently outperforms by a large margin in sensitivity while maintaining better or comparable precision. In particular, when the annotations are not provided, TERRACE can assemble 123%-412% more correct circRNAs than state-of-the-art methods on human tissues. TERRACE presents a major leap on assembling full-length circRNAs from RNA-seq data, and we expect it to be widely used in the downstream research on circRNAs.

FL-circAS: an integrative resource and analysis for full-length sequences and alternative splicing of circular RNAs with nanopore sequencing

Circular RNAs (circRNAs) are RNA molecules with a continuous loop structure characterized by back-splice junctions (BSJs). While analyses of short-read RNA sequencing have identified millions of BSJ events, it is inherently challenging to determine exact full-length sequences and alternatively spliced (AS) isoforms of circRNAs. Recent advances in nanopore long-read sequencing with circRNA enrichment bring an unprecedented opportunity for investigating the issues. Here, we developed FL-circAS (https://cosbi.ee.ncku.edu.tw/FL-circAS/), which collected such long-read sequencing data of 20 cell lines/tissues and thereby identified 884 636 BSJs with 1 853 692 full-length circRNA isoforms in human and 115 173 BSJs with 135 617 full-length circRNA isoforms in mouse. FL-circAS also provides multiple circRNA features. For circRNA expression, FL-circAS calculates expression levels for each circRNA isoform, cell line/tissue specificity at both the BSJ and isoform levels, and AS entropy for each BSJ across samples. For circRNA biogenesis, FL-circAS identifies reverse complementary sequences and RNA binding protein (RBP) binding sites residing in flanking sequences of BSJs. For functional patterns, FL-circAS identifies potential microRNA/RBP binding sites and several types of evidence for circRNA translation on each full-length circRNA isoform. FL-circAS provides user-friendly interfaces for browsing, searching, analyzing, and downloading data, serving as the first resource for discovering full-length circRNAs at the isoform level.

New insight into circRNAs: characterization, strategies, and biomedical applications

Circular RNAs (circRNAs) are a class of covalently closed, endogenous ncRNAs. Most circRNAs are derived from exonic or intronic sequences by precursor RNA back-splicing. Advanced high-throughput RNA sequencing and experimental technologies have enabled the extensive identification and characterization of circRNAs, such as novel types of biogenesis, tissue-specific and cell-specific expression patterns, epigenetic regulation, translation potential, localization and metabolism. Increasing evidence has revealed that circRNAs participate in diverse cellular processes, and their dysregulation is involved in the pathogenesis of various diseases, particularly cancer. In this review, we systematically discuss the characterization of circRNAs, databases, challenges for circRNA discovery, new insight into strategies used in circRNA studies and biomedical applications. Although recent studies have advanced the understanding of circRNAs, advanced knowledge and approaches for circRNA annotation, functional characterization and biomedical applications are continuously needed to provide new insights into circRNAs. The emergence of circRNA-based protein translation strategy will be a promising direction in the field of biomedicine.

JCcirc: circRNA full-length sequence assembly through integrated junction contigs

Recent studies have shed light on the potential of circular RNA (circRNA) as a biomarker for disease diagnosis and as a nucleic acid vaccine. The exploration of these functionalities requires correct circRNA full-length sequences; however, existing assembly tools can only correctly assemble some circRNAs, and their performance can be further improved. Here, we introduce a novel feature known as the junction contig (JC), which is an extension of the back-splice junction (BSJ). Leveraging the strengths of both BSJ and JC, we present a novel method called JCcirc (https://github.com/cbbzhang/JCcirc). It enables efficient reconstruction of all types of circRNA full-length sequences and their alternative isoforms using splice graphs and fragment coverage. Our findings demonstrate the superiority of JCcirc over existing methods on human simulation datasets, and its average F1 score surpasses CircAST by 0.40 and outperforms both CIRI-full and circRNAfull by 0.13. For circRNAs below 400 bp, 400-800 bp, 800 bp-1200 bp and above 1200 bp, the correct assembly rates are 0.13, 0.09, 0.04 and 0.03 higher, respectively, than those achieved by existing methods. Moreover, JCcirc also outperforms existing assembly tools on other five model species datasets and real sequencing datasets. These results show that JCcirc is a robust tool for accurately assembling circRNA full-length sequences, laying the foundation for the functional analysis of circRNAs.

Dysregulation of circular RNAs in inflammation and cancers

Emerging lines of evidence have shown that the production of the covalently closed single-stranded circular RNAs is not splicing errors, but rather a regulated process with distinct biogenesis and turnover. Circular RNAs are expressed in a cell type- and tissue-specific manner and often localize to specific subcellular regions or organelles for functions. The dysregulation of circular RNAs from birth to death is linked to the pathogenesis and progression of diverse diseases. This review outlines how aberrant circular RNA biogenesis, subcellular location, and degradation are linked to disease progression, focusing on metaflammation and cancers. We also discuss potential therapeutic strategies and obstacles in targeting such disease-related circular RNAs.

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.

Full-length circular RNA profiling by nanopore sequencing with CIRI-long

Circular RNAs (circRNAs) have important roles in regulating developmental processes and disease progression. As most circRNA sequences are highly similar to their cognate linear transcripts, the current short-read sequencing-based methods rely on the back-spliced junction signal for distinguishing circular and linear reads, which does not allow circRNAs' full-length structure to be effectively reconstructed. Here we describe a long-read sequencing-based protocol, CIRI-long, for the detection of full-length circular RNAs. The CIRI-long protocol combines rolling circular reverse transcription and nanopore sequencing to capture full-length circRNA sequences. After poly(A) tailing, RNase R treatment, and size selection of polymerase chain reaction products, CIRI-long achieves an increased percentage (6%) of circular reads in the constructed library, which is 20-fold higher compared with previous Illumina-based strategies. This method can be applied in cell lines or tissue samples, enabling accurate detection of full-length circRNAs in the range of 100-3,000 bp. The entire protocol can be completed in 1 d, and can be scaled up for large-scale analysis using the nanopore barcoding kit and PromethION sequencing device. CIRI-long can serve as an effective and user-friendly protocol for characterizing full-length circRNAs, generating direct and convincing evidence for the existence of detected circRNAs. The analytical pipeline offers convenient functions for identification of full-length circRNA isoforms and integration of multiple datasets. The assembled full-length transcripts and their splicing patterns provide indispensable information to explore the biological function of circRNAs.

Łabaj P, Mangul S. RNA-seq data science: From raw data to effective interpretation

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Characterization of circular RNAs with advanced sequencing technologies in human complex diseases

Circular RNAs (circRNAs) are one category of non-coding RNAs that do not possess 5' caps and 3' free ends. Instead, they are derived in closed circle forms from pre-mRNAs by a non-canonical splicing mechanism named "back-splicing." CircRNAs were discovered four decades ago, initially called "scrambled exons." Compared to linear RNAs, the expression levels of circRNAs are considerably lower, and it is challenging to identify circRNAs specifically. Thus, the biological relevance of circRNAs has been underappreciated until the advancement of next generation sequencing (NGS) technology. The biological insights of circRNAs, such as their tissue-specific expression patterns, biogenesis factors, and functional effects in complex diseases, namely human cancers, have been extensively explored in the last decade. With the invention of the third generation sequencing (TGS) with longer sequencing reads and newly designed strategies to characterize full-length circRNAs, the panorama of circRNAs in human complex diseases could be further unveiled. In this review, we first introduce the history of circular RNA detection. Next, we describe widely adopted NGS-based methods and the recently established TGS-based approaches capable of characterizing circRNAs in full-length. We then summarize data resources and representative circRNA functional studies related to human complex diseases. In the last section, we reviewed computational tools and discuss the potential advantages of utilizing advanced sequencing approaches to a functional interpretation of full-length circRNAs in complex diseases. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Disease.

CYCLeR-a novel tool for the full isoform assembly and quantification of circRNAs

Splicing is one key mechanism determining the state of any eukaryotic cell. Apart from linear splice variants, circular splice variants (circRNAs) can arise via non-canonical splicing involving a back-splice junction (BSJ). Most existing methods only identify circRNAs via the corresponding BSJ, but do not aim to estimate their full sequence identity or to identify different, alternatively spliced circular isoforms arising from the same BSJ. We here present CYCLeR, the first computational method for identifying the full sequence identity of new and alternatively spliced circRNAs and their abundances while simultaneously co-estimating the abundances of known linear splicing isoforms. We show that CYCLeR significantly outperforms existing methods in terms of F score and quantification of transcripts in simulated data. In a in a comparative study with long-read data, we also show the advantages of CYCLeR compared to existing methods. When analysing Drosophila melanogaster data, CYCLeR uncovers biological patterns of circRNA expression that other methods fail to observe.

A Review and In Silico Analysis of Tissue and Exosomal Circular RNAs: Opportunities and Challenges in Thyroid Cancer

Simple Summary

Thyroid cancer is the most common endocrine neoplasm. Recently, knowledge of the molecular genetic changes of thyroid cancer has dramatically improved. Understanding the roles of these molecular changes in thyroid cancer tumorigenesis and progression is essential in developing a successful treatment strategy and improving disease outcomes. As a family of non-coding RNAs, circular RNAs (circRNAs) have been involved in several aspects of the physiological and pathological processes of the cells. The roles of circRNAs in cancer development and progress are evident. In the current review, we aimed to explore the clinical potential of circRNAs as potential diagnostic, prognostic, and therapeutic targets in thyroid cancer. Furthermore, screening the genome-wide circRNAs and performing functional enrichment analyses for all associated dysregulated circRNAs in thyroid cancer have been done. Given the unique advantages circRNAs have, such as superior stability, higher abundance, and presence in different body fluids, this family of non-coding RNAs could be promising diagnostic and prognostic biomarkers and potential therapeutic targets for thyroid cancer.

Abstract

Thyroid cancer (TC) is the most common endocrine tumor. The genetic and epigenetic molecular alterations of TC have become more evident in recent years. However, a deeper understanding of the roles these molecular changes play in TC tumorigenesis and progression is essential in developing a successful treatment strategy and improving patients’ prognoses. Circular RNAs (circRNAs), a family of non-coding RNAs, have been implicated in several aspects of carcinogenesis in multiple cancers, including TC. In the current review, we aimed to explore the clinical potential of circRNAs as putative diagnostic, prognostic, and therapeutic targets in TC. The current analyses, including genome-wide circRNA screening and functional enrichment for all deregulated circRNA expression signatures, show that circRNAs display atypical contributions, such as sponging for microRNAs, regulating transcription and translation processes, and decoying for proteins. Given their exceptional clinical advantages, such as higher stability, wider abundance, and occurrence in several body fluids, circRNAs are promising prognostic and theranostic biomarkers for TC.

DEBKS: A Tool to Detect Differentially Expressed Circular RNAs

Circular RNAs (circRNAs) are involved in various biological processes and disease pathogenesis. However, only a small number of functional circRNAs have been identified among hundreds of thousands of circRNA species, partly because most current methods are based on circular junction counts and overlook the fact that a circRNA is formed from the host gene by back-splicing (BS). To distinguish the expression difference originating from BS or the host gene, we present differentially expressed back-splicing (DEBKS), a software program to streamline the discovery of differential BS events between two rRNA-depleted RNA sequencing (RNA-seq) sample groups. By applying to real and simulated data and employing RT-qPCR for validation, we demonstrate that DEBKS is efficient and accurate in detecting circRNAs with differential BS events between paired and unpaired sample groups. DEBKS is available at https://github.com/yangence/DEBKS as open-source software.

circFL-seq, A Full-length circRNA Sequencing Method

Due to overlapping sequences with linear cognates, identifying internal sequences of circular RNA (circRNA) remains a challenge. Recently, we have developed a full-length circRNA sequencing method (circFL-seq) and computational pipeline, to profile ordinary and fusion circRNA at the isoform level. Compared to short-read RNA-seq, rolling circular reverse transcription and nanopore long-read sequencing of circFL-seq make circRNA reads more than tenfold enriched, and show advantages for identification of both short (<100 nt) and long (>2,000 nt) circRNA transcripts. circFL-seq allows identification of differential alternative splicing suggested wide application prospects for functional studies of internal sequences in circRNAs. In addition, the experimental protocol and computational pipeline of circFL-seq shows better sensitivity and precision for identification of back-splicing junctions than current long-read sequencing methods. Together, the accurate identification and quantification of full-length circRNAs makes circFL-seq a potential tool for large-scale screening of functional circRNAs.

The emerging roles of circRNAs in cancer and oncology

Over the past decade, circular RNAs (circRNAs) have emerged as a large class of primarily non-coding RNA molecules, many of which have key roles in cancer development and progression through diverse mechanisms of action. CircRNAs often have tissue-restricted and cancer-specific expression patterns, and accumulating data suggest that these molecules are of potential clinical relevance and utility. In particular, circRNAs have strong potential as diagnostic, prognostic and predictive biomarkers, which is underscored by their detectability in liquid biopsy samples such as in plasma, saliva and urine. However, technical issues in the detection and assessment of circRNAs as well as biological knowledge gaps need to be addressed to move this relatively young field of research forward and bring circRNAs to the forefront of clinical practice. Herein, we review the current knowledge regarding circRNA biogenesis, regulation and functions in cancer as well as their clinical potential as biomarkers, therapeutic agents and drug targets.

Evaluation of CircRNA Sequence Assembly Methods Using Long Reads

The functional study on circRNAs has been increasing in the past decade due to its important roles in micro RNA sponge, protein coding, the initiation, and progression of diseases. The study of circRNA functions depends on the full-length sequences of circRNA, and current sequence assembly methods based on short reads face challenges due to the existence of linear transcript. Long reads produced by long-read sequencing techniques such as Nanopore technology can cover full-length sequences of circRNA and therefore can be used to evaluate the correctness and completeness of circRNA full sequences assembled from short reads of the same sample. Using long reads of the same samples, one from human and the other from mouse, we have comprehensively evaluated the performance of several well-known circRNA sequence assembly algorithms based on short reads, including circseq_cup, CIRI_full, and CircAST. Based on the F1 score, the performance of CIRI-full was better in human datasets, whereas in mouse datasets CircAST was better. In general, each algorithm was developed to handle special situations or circumstances. Our results indicated that no single assembly algorithm generated better performance in all cases. Therefore, these assembly algorithms should be used together for reliable full-length circRNA sequence reconstruction. After analyzing the results, we have introduced a screening protocol that selects out exonic circRNAs with full-length sequences consisting of all exons between back splice sites as the final result. After screening, CIRI-full showed better performance for both human and mouse datasets. The average F1 score of CIRI-full over four circRNA identification algorithms increased from 0.4788 to 0.5069 in human datasets, and it increased from 0.2995 to 0.4223 in mouse datasets.

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.

Sensitive, reliable and robust circRNA detection from RNA-seq with CirComPara2

Circular RNAs (circRNAs) are a large class of covalently closed RNA molecules originating by a process called back-splicing. CircRNAs are emerging as functional RNAs involved in the regulation of biological processes as well as in disease and cancer mechanisms. Current computational methods for circRNA identification from RNA-seq experiments are characterized by low discovery rates and performance dependent on the analysed data set. We developed CirComPara2 (https://github.com/egaffo/CirComPara2), a new automated computational pipeline for circRNA discovery and quantification, which consistently achieves high recall rates without losing precision by combining multiple circRNA detection methods. In our benchmark analysis, CirComPara2 outperformed state-of-the-art circRNA discovery tools and proved to be a reliable and robust method for comprehensive transcriptome characterization.

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.

circFL-seq reveals full-length circular RNAs with rolling circular reverse transcription and nanopore sequencing

Circular RNAs (circRNAs) act through multiple mechanisms via their sequence features to fine-tune gene expression networks. Due to overlapping sequences with linear cognates, identifying internal sequences of circRNAs remains a challenge, which hinders a comprehensive understanding of circRNA functions and mechanisms. Here, based on rolling circular reverse transcription and nanopore sequencing, we developed circFL-seq, a full-length circRNA sequencing method, to profile circRNA at the isoform level. With a customized computational pipeline to directly identify full-length sequences from rolling circular reads, we reconstructed 77,606 high-quality circRNAs from seven human cell lines and two human tissues. circFL-seq benefits from rolling circles and long-read sequencing, and the results showed more than tenfold enrichment of circRNA reads and advantages for both detection and quantification at the isoform level compared to those for short-read RNA sequencing. The concordance of the RT-qPCR and circFL-seq results for the identification of differential alternative splicing suggested wide application prospects for functional studies of internal variants in circRNAs. Moreover, the detection of fusion circRNAs at the omics scale may further expand the application of circFL-seq. Taken together, the accurate identification and quantification of full-length circRNAs make circFL-seq a potential tool for large-scale screening of functional circRNAs.

Recent research progress on circular RNAs: Biogenesis, properties, functions, and therapeutic potential

Circular RNAs (circRNAs), an emerging family member of RNAs, have gained importance in research due to their new functional roles in cellular physiology and disease progression. circRNAs are usually available in a wide range of cells and have shown tissue-specific expression as well as developmental specific expression. circRNAs are characterized by structural stability, conservation, and high abundance in the cell. In this review, we discuss the different models of biogenesis. The properties of circRNAs such as localization, structure and conserved pattern, stability, and expression specificity are also been illustrated. Furthermore, we discuss the biological functions of circRNAs such as microRNA (miRNA) sponging, cell cycle regulation, cell-to-cell communication, transcription regulation, translational regulation, disease diagnosis, and therapeutic potential. Finally, we discuss the recent research progress and future perspective of circRNAs. This review provides an understanding of potential diagnostic markers and the therapeutic potential of circRNAs, which are emerging daily.

Emerging Role of Circular RNA-Protein Interactions

Circular RNAs (circRNAs) are emerging as novel regulators of gene expression in various biological processes. CircRNAs regulate gene expression by interacting with cellular regulators such as microRNAs and RNA binding proteins (RBPs) to regulate downstream gene expression. The accumulation of high-throughput RNA-protein interaction data revealed the interaction of RBPs with the coding and noncoding RNAs, including recently discovered circRNAs. RBPs are a large family of proteins known to play a critical role in gene expression by modulating RNA splicing, nuclear export, mRNA stability, localization, and translation. However, the interaction of RBPs with circRNAs and their implications on circRNA biogenesis and function has been emerging in the last few years. Recent studies suggest that circRNA interaction with target proteins modulates the interaction of the protein with downstream target mRNAs or proteins. This review outlines the emerging mechanisms of circRNA-protein interactions and their functional role in cell physiology.

Circular RNA translation, a path to hidden proteome

Functional proteins in the cell are translated from the messenger RNA (mRNA) molecules, constituting less than 5% of the cellular transcriptome. The majority of the RNA molecules in the cell are noncoding RNAs, including rRNA, tRNA, snRNA, piRNA, lncRNA, microRNA, and poorly characterized circular RNAs (circRNAs). Recent studies established that circRNAs regulate gene expression by associating with RNA-binding proteins and microRNAs. With the growing understanding of circRNA functions, a subset of circRNAs has been reported to translate into proteins. Interestingly, the presence of Open Reading Frames (ORFs), N6-methyladenosine (m6A) modifications, and internal ribosomal entry sites (IRES) in the circRNA sequences indicate their coding potential through the cap-independent translation initiation mechanism. The purpose of this review is to highlight the mechanism of circRNA translation and the importance of circRNA-encoded proteins (circ-proteins) in cellular physiology and pathology. Here, we discuss the computational and molecular methods currently utilized to systematically identify translatable circRNAs and the functional characterization of the circ-proteins. We foresee that the ongoing and future studies on circRNA translation will uncover the hidden proteome and their therapeutic implications in human health. This article is categorized under: RNA Methods > RNA Analyses in Cells Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs Translation > Mechanisms.

Comprehensive profiling of circular RNAs with nanopore sequencing and CIRI-long

Reconstructing the sequence of circular RNAs (circRNAs) from short RNA sequencing reads has proved challenging given the similarity of circRNAs and their corresponding linear messenger RNAs. Previous sequencing methods were unable to achieve high-throughput detection of full-length circRNAs. Here we describe a protocol for enrichment and full-length sequencing of circRNA isoforms using nanopore technology. Circular reverse transcription and size selection achieves a 20-fold higher enrichment of circRNAs from total RNA compared to previous methods. We developed an algorithm, called circRNA identifier using long-read sequencing data (CIRI-long), to reconstruct the sequence of circRNAs. The workflow was validated with simulated data and by comparison to Illumina sequencing as well as quantitative real-time RT-PCR. We used CIRI-long to analyze adult mouse brain samples and systematically profile circRNAs, including mitochondria-derived and transcriptional read-through circRNAs. We identified a new type of intronic self-ligated circRNA that exhibits special splicing and expression patterns. Our method takes advantage of nanopore long reads and enables unbiased reconstruction of full-length circRNA sequences.

AQUARIUM: accurate quantification of circular isoforms using model-based strategy

SUMMARY

Currently, most computational methods estimate the expression of circular RNAs (circRNAs) using the number of sequencing reads that support back-splicing junctions (BSJ) in RNA-seq data, which may introduce biased estimation of circRNA expression due to the uneven distribution of sequencing reads. To overcome this, we previously developed a model-based strategy for circRNA quantification, enabling consideration of sequencing reads from the entire transcript. Yet, the lack of exact transcript structure of circRNAs may limit its accuracy. Here, we proposed a substantially improved circRNA quantification tool, AQUARIUM, by introducing the full-length RNA structure of circular isoforms. We assessed its performance in circRNA quantification using both biological and simulated rRNA-depleted RNA-seq datasets, and demonstrated its superior performance at both BSJ and isoform level.

AVAILABILITY AND IMPLEMENTATION

AQUARIUM is freely available at https://github.com/wanjun-group-seu/AQUARIUM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role of Virally Encoded Circular RNAs in the Pathogenicity of Human Oncogenic Viruses

Human oncogenic viruses are a group of important pathogens that etiologically contribute to at least 12% of total cancer cases in the world. As an emerging class of non-linear regulatory RNA molecules, circular RNAs (circRNAs) have gained increasing attention as a crucial player in the regulation of signaling pathways involved in viral infection and oncogenesis. With the assistance of current circRNA enrichment and detection technologies, numerous novel virally-encoded circRNAs (vcircRNAs) have been identified in the human oncogenic viruses, initiating an exciting new era of vcircRNA research. In this review, we discuss the current understanding of the roles of vcircRNAs in the respective viral infection cycles and in virus-associated pathogenesis.

Reconstruction of circular RNAs using Illumina and Nanopore RNA-seq datasets

High-throughput RNA sequencing has enabled the extensive detection of circular RNAs (circRNAs) in eukaryotic organisms. However, most circRNAs are derived from exonic regions and possess sequences that are highly overlapped to their cognate linear mRNAs, which makes the reconstruction of the internal structure and full-length circular transcripts a challenging aspect in circRNA studies. To solve this problem, we provide a step-by-step protocol for the full-length reconstruction of circRNAs using CIRI-full and CIRI-long in Illumina and Nanopore RNA-seq libraries. By combining experimental and computational methods, we are able to effectively characterize the full-length landscape of circRNAs, which provide an important basis to explore the biogenesis and biological function of circRNAs.

Titin Circular RNAs Create a Back-Splice Motif Essential for SRSF10 Splicing

Supplemental Digital Content is available in the text.

Background:

TTN (Titin), the largest protein in humans, forms the molecular spring that spans half of the sarcomere to provide passive elasticity to the cardiomyocyte. Mutations that disrupt the TTN transcript are the most frequent cause of hereditary heart failure. We showed before that TTN produces a class of circular RNAs (circRNAs) that depend on RBM20 to be formed. In this study, we show that the back-splice junction formed by this class of circRNAs creates a unique motif that binds SRSF10 to enable it to regulate splicing. Furthermore, we show that one of these circRNAs (cTTN1) distorts both localization of and splicing by RBM20.

Methods:

We calculated genetic constraint of the identified motif in 125 748 exomes collected from the gnomAD database. Furthermore, we focused on the highest expressed RBM20-dependent circRNA in the human heart, which we named cTTN1. We used shRNAs directed to the back-splice junction to induce selective loss of cTTN1 in human induced pluripotent stem cell–derived cardiomyocytes.

Results:

Human genetics suggests reduced genetic tolerance of the generated motif, indicating that mutations in this motif might lead to disease. RNA immunoprecipitation confirmed binding of circRNAs with this motif to SRSF10. Selective loss of cTTN1 in human induced pluripotent stem cell–derived cardiomyocytes induced structural abnormalities, apoptosis, and reduced contractile force in engineered heart tissue. In line with its SRSF10 binding, loss of cTTN1 caused abnormal splicing of important cardiomyocyte SRSF10 targets such as MEF2A and CASQ2. Strikingly, loss of cTTN1 also caused abnormal splicing of TTN itself. Mechanistically, we show that loss of cTTN1 distorts both localization of and splicing by RBM20.

Conclusions:

We demonstrate that circRNAs formed from the TTN transcript are essential for normal splicing of key muscle genes by enabling splice regulators RBM20 and SRSF10. This shows that the TTN transcript also has regulatory roles, besides its well-known signaling and structural function. In addition, we demonstrate that the specific sequence created by the back-splice junction of these circRNAs has important functions. This highlights the existence of functionally important sequences that cannot be recognized as such in the human genome but provides an as-yet unrecognized source for functional sequence variation.

isoCirc catalogs full-length circular RNA isoforms in human transcriptomes

Circular RNAs (circRNAs) have emerged as an important class of functional RNA molecules. Short-read RNA sequencing (RNA-seq) is a widely used strategy to identify circRNAs. However, an inherent limitation of short-read RNA-seq is that it does not experimentally determine the full-length sequences and exact exonic compositions of circRNAs. Here, we report isoCirc, a strategy for sequencing full-length circRNA isoforms, using rolling circle amplification followed by nanopore long-read sequencing. We describe an integrated computational pipeline to reliably characterize full-length circRNA isoforms using isoCirc data. Using isoCirc, we generate a comprehensive catalog of 107,147 full-length circRNA isoforms across 12 human tissues and one human cell line (HEK293), including 40,628 isoforms ≥500 nt in length. We identify widespread alternative splicing events within the internal part of circRNAs, including 720 retained intron events corresponding to a class of exon-intron circRNAs (EIciRNAs). Collectively, isoCirc and the companion dataset provide a useful strategy and resource for studying circRNAs in human transcriptomes.

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.

Viruses join the circular RNA world

Circular RNAs (circRNAs) are a recently discovered class of noncoding RNAs found in many species across the eukaryotic kingdom. These intriguing RNA species are formed through a unique mechanism that is known as back splicing in which the 5′ and 3′ termini are covalently joined. Recent research has revealed that viruses also encode a repertoire of circRNAs. Some of these viral circRNAs are abundantly expressed and are reported to play a role in disease pathogenesis. A growing number of studies also indicate that host circRNAs are involved in immune responses against virus infections with either an antiviral or proviral role. In this review, we briefly introduce circRNA, its biogenesis, and mechanism of action. We go on to summarize the latest research on the expression, regulation, and functions of viral and host‐encoded circRNAs during the host–virus interaction, with the aim of highlighting the potential of viral and host circRNAs as a suitable target for diagnostic biomarker development and therapeutic treatment of viral‐associated diseases. We conclude by discussing the current limitations in knowledge and significance of elucidating the roles of circRNAs in host–virus interactions, as well as future directions for this emerging field.

Genome-Wide Analysis of the Expression of Circular RNA Full-Length Transcripts and Construction of the circRNA-miRNA-mRNA Network in Cervical Cancer

Increasing evidence suggests that circular RNA (circRNA) plays an important role in tumorigenesis by regulating gene expression at the transcriptional and post-transcriptional levels. Alternative splicing events permit multiple transcript isoforms of circRNA to be produced; however, changes in the expression of circRNA full-length transcripts in cervical cancer remain unclear. Here, we systematically explored the dysregulation circRNA full-length transcripts and constructed an improved circRNA-miRNA-mRNA regulatory network to provide potential biomarkers and possible treatment targets in cervical cancer. We identified 9359 circular full-length transcripts from RNase R-treated RNA-seq data in cervical cancer, of which 353 circular full-length transcripts were significantly differentially expressed (DE) between the tumor and normal group. A total of 881 DE mRNA transcript isoforms were also identified from total RNA-seq data in cervical cancer, of which 421 (47.8%) transcript isoforms were up-regulated, and 460 (52.2%) transcript isoforms were down-regulated in tumor samples. Two circRNA-miRNA-mRNA competitively regulated networks, including 33 circRNA transcripts, 2 miRNAs, and 189 mRNA transcripts were constructed. Three genes (COPE, RAB3B, and TFPI) in the network were significantly associated with overall survival (P < 0.05), which indicated that these genes could act as prognostic biomarkers for patients with cervical cancer. Our study revealed genome-wide differential expression of full-length circRNA transcripts and constructed a more accurate circRNA-miRNA-mRNA network at the full-length transcript expression level in cervical cancer. CircRNA may thus be involved in the development of cervical cancer by regulating the expression of COPE, RAB3B, and TFPI. However, the specific regulatory mechanism in cervical cancer requires further study.

Perspectives on Circular RNAs as Prostate Cancer Biomarkers

High throughput RNA sequencing has revealed the existence of abundant circular RNAs (circRNAs) that are cell lineage-specific and have been implicated in human diseases. CircRNAs are resistant to exonuclease digestion, can carry genetic information of oncogenes, and are enriched in exosome to be transported from tissues into various body fluids. These properties make circRNAs ideal non-invasive diagnostic biomarkers for disease detection. Furthermore, many circRNAs have been demonstrated to possess biological functions in relevant cells, suggesting that they may also be potential therapeutic targets and reagents. However, our knowledge of circRNAs is still at an infant stage and far from being translated into clinics. Here, we review circRNAs in the disease setting of prostate cancer. We start by introducing the basic knowledge of circRNAs, followed by summarizing opportunities of circRNAs to be prostate cancer biomarkers, and discuss current challenges in circRNA research and outlook of future directions in translating current knowledge about circRNA into clinical practice.

The mechanism and detection of alternative splicing events in circular RNAs

Circular RNAs (circRNAs) are considered as functional biomolecules with tissue/development-specific expression patterns. Generally, a single gene may generate multiple circRNA variants by alternative splicing, which contain different combinations of exons and/or introns. Due to the low abundance of circRNAs as well as overlapped with their linear counterparts, circRNA enrichment protocol is needed prior to sequencing. Compared with numerous algorithms, which use back-splicing reads for detection and functional characterization of circRNAs, original bioinformatic analyzing tools have been developed to large-scale determination of full-length circRNAs and accurate quantification. This review provides insights into the complexity of circRNA biogenesis and surveys the recent progresses in the experimental and bioinformatic methodologies that focus on accurately full-length circRNAs identification.