A comprehensive overview and evaluation of circular RNA detection tools

Current Understandings and Open Hypotheses on Extracellular Circular RNAs

Circular RNAs (circRNAs) are closed RNA loops present in humans and other organisms. Various circRNAs have an essential role in diseases, including cancer. Cells can release circRNAs into the extracellular space of adjacent biofluids and can be present in extracellular vesicles. Due to their circular nature, extracellular circRNAs (excircRNAs) are more stable than their linear counterparts and are abundant in many biofluids, such as blood plasma and urine. circRNAs' link with disease suggests their extracellular counterparts have high biomarker potential. However, circRNAs and the extracellular space are challenging research domains, as they consist of complex biological systems plagued with nomenclature issues and a wide variety of protocols with different advantages and disadvantages. Here, we summarize what is known about excircRNAs, the current challenges in the field, and what is needed to improve extracellular circRNA research.

Identifying circRNA-disease association based on relational graph attention network and hypergraph attention network

In recent years, with the in-depth study of circRNA, scholars have begun to discover a synergistic relationship between circRNA and microorganisms. Traditional wet lab experiments in biology require expensive financial, material, and human resources to investigate the relationship between circRNA and diseases. Therefore, we propose a new predictive model for inferring the association between circRNA and diseases, called HAGACDA. Specifically, we first aggregate the unique features of circRNA and diseases themselves through singular value decomposition, Pearson similarity, and the biological information characteristics of circRNA and diseases. Utilizing the competitive relationships between miRNA and other microorganisms, we construct a circRNA-miRNA-disease multi-source heterogeneous network. Subsequently, we use a relational graph attention network to aggregate features based on the structural connections between different nodes. To address the inherent limitations in capturing high-order patterns in edge sets, we integrate a hypergraph attention network to extract features of circRNA and diseases. Finally, association prediction scores for node pairs are obtained through a multilayer perceptron. We conducted a comprehensive analysis of the model, including comparative experiments and case studies. Experimental results demonstrate that our model accurately predicts the association between circRNA and diseases.

Comprehensive circular RNA profiling in various sheep tissues

Despite the scientific relevance of circular RNAs (circRNAs), the study of these RNAs in non-model organisms, especially in sheep, is still in its infancy. On the other hand, while some studies have focused on sheep circRNA identification in a limited number of tissues, there is a lack of comprehensive analysis that profile circRNA expression patterns across the tissues not yet investigated. In this study, 61 public RNA sequencing datasets from 12 different tissues were uniformly analyzed to identify circRNAs, profile their expression and investigate their various characteristics. We reported for the first time a circRNA expression landscape with functional annotation in sheep tissues not yet investigated including hippocampus, BonMarrowMacrophage, left-ventricle, thymus, ileum, reticulum and 23-day-embryo. A stringent computational pipeline was employed and 8919 exon-derived circRNAs with high confidence were identified, including 88 novel circRNAs. Tissue-specificity analysis revealed that 3059 circRNAs were tissue-specific, which were also more specific to the tissues than linear RNAs. The highest number of tissue-specific circRNAs was found in kidney, hippocampus and thymus, respectively. Co-expression analysis revealed that expression of circRNAs may not be affected by their host genes. While most of the host genes produced more than one isoform, only one isoform had dominant expression across the tissues. The host genes of the tissue-specific circRNAs were significantly enriched in biological/pathways terms linked to the important functions of their corresponding tissues, suggesting potential roles of circRNAs in modulating physiological activity of those tissues. Interestingly, functional terms related to the regulation and various signaling pathways were significantly enriched in all tissues, suggesting some common regulatory mechanisms of circRNAs to modulate the physiological functions of tissues. Finding of the present study provide a valuable resource for depicting the complexity of circRNAs expression across tissues of sheep, which can be useful for the field of sheep genomic and veterinary research.

GASIDN: identification of sub-Golgi proteins with multi-scale feature fusion

The Golgi apparatus is a crucial component of the inner membrane system in eukaryotic cells, playing a central role in protein biosynthesis. Dysfunction of the Golgi apparatus has been linked to neurodegenerative diseases. Accurate identification of sub-Golgi protein types is therefore essential for developing effective treatments for such diseases. Due to the expensive and time-consuming nature of experimental methods for identifying sub-Golgi protein types, various computational methods have been developed as identification tools. However, the majority of these methods rely solely on neighboring features in the protein sequence and neglect the crucial spatial structure information of the protein.To discover alternative methods for accurately identifying sub-Golgi proteins, we have developed a model called GASIDN. The GASIDN model extracts multi-dimension features by utilizing a 1D convolution module on protein sequences and a graph learning module on contact maps constructed from AlphaFold2.The model utilizes the deep representation learning model SeqVec to initialize protein sequences. GASIDN achieved accuracy values of 98.4% and 96.4% in independent testing and ten-fold cross-validation, respectively, outperforming the majority of previous predictors. To the best of our knowledge, this is the first method that utilizes multi-scale feature fusion to identify and locate sub-Golgi proteins. In order to assess the generalizability and scalability of our model, we conducted experiments to apply it in the identification of proteins from other organelles, including plant vacuoles and peroxisomes. The results obtained from these experiments demonstrated promising outcomes, indicating the effectiveness and versatility of our model. The source code and datasets can be accessed at https://github.com/SJNNNN/GASIDN .

Use of synthetic circular RNA spike-ins (SynCRS) for normalization of circular RNA sequencing data

High-throughput RNA sequencing enables the quantification of transcript abundance and the identification of novel transcripts in biological samples. These include circular RNAs (circRNAs), a family of alternatively spliced RNA molecules that form a continuous loop. However, quantification and comparison of circRNAs between RNA sequencing libraries remain challenging due to confounding errors introduced during exonuclease digestion, library preparation and RNA sequencing itself. Here we describe a set of synthetic circRNA spike-ins-termed 'SynCRS'-that can be added directly to purified RNA samples before exonuclease digestion and library preparation. SynCRS, introduced either individually or in combinations of varying size and abundance, can be integrated into all next-generation sequencing workflows and, critically, facilitate the quantitative calibration of circRNA transcript abundance between samples, tissue types, species and laboratories. Our step-by-step protocol details the generation of SynCRS and guides users on the stoichiometry of SynCRS spike-in to RNA samples, followed by the bioinformatic steps required to facilitate quantitative comparisons of circRNAs between libraries. The laboratory steps to produce the SynCRS require an additional 3 d on top of the high throughput circRNA sequencing and bioinformatics. The protocol is suitable for users with basic experience in molecular biology and bioinformatics.

Long-range alternative splicing contributes to neoantigen specificity in glioblastoma

Recent advances in neoantigen research have accelerated the development of immunotherapies for cancers, such as glioblastoma (GBM). Neoantigens resulting from genomic mutations and dysregulated alternative splicing have been studied in GBM. However, these studies have primarily focused on annotated alternatively-spliced transcripts, leaving non-annotated transcripts largely unexplored. Circular ribonucleic acids (circRNAs), abnormally regulated in tumors, are correlated with the presence of non-annotated linear transcripts with exon skipping events. But the extent to which these linear transcripts truly exist and their functions in cancer immunotherapies remain unknown. Here, we found the ubiquitous co-occurrence of circRNA biogenesis and alternative splicing across various tumor types, resulting in large amounts of long-range alternatively-spliced transcripts (LRs). By comparing tumor and healthy tissues, we identified tumor-specific LRs more abundant in GBM than in normal tissues and other tumor types. This may be attributable to the upregulation of the protein quaking in GBM, which is reported to promote circRNA biogenesis. In total, we identified 1057 specific and recurrent LRs in GBM. Through in silico translation prediction and MS-based immunopeptidome analysis, 16 major histocompatibility complex class I-associated peptides were identified as potential immunotherapy targets in GBM. This study revealed long-range alternatively-spliced transcripts specifically upregulated in GBM may serve as recurrent, immunogenic tumor-specific antigens.

iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites.

Circular RNA regulatory role in pathological cardiac remodelling

Cardiac remodelling involves structural, cellular and molecular alterations in the heart after injury, resulting in progressive loss of heart function and ultimately leading to heart failure. Circular RNAs (circRNAs) are a recently rediscovered class of non-coding RNAs that play regulatory roles in the pathogenesis of cardiovascular diseases, including heart failure. Thus, a more comprehensive understanding of the role of circRNAs in the processes governing cardiac remodelling may set the ground for the development of circRNA-based diagnostic and therapeutic strategies. In this review, the current knowledge about circRNA origin, conservation, characteristics and function is summarized. Bioinformatics and wet-lab methods used in circRNA research are discussed. The regulatory function of circRNAs in cardiac remodelling mechanisms such as cell death, cardiomyocyte hypertrophy, inflammation, fibrosis and metabolism is highlighted. Finally, key challenges and opportunities in circRNA research are discussed, and orientations for future work to address the pharmacological potential of circRNAs in heart failure are proposed.

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.

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information

Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.

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.

Role of the circular RNA regulatory network in the pathogenesis of biliary atresia

Circular RNAs (circRNAs) serve an essential role in the occurrence and development of cholangiocarcinoma, but the expression and function of circRNA in biliary atresia (BA) is not clear. In the present study, circRNA expression profiles were investigated in the liver tissues of patients with BA as well as in the choledochal cyst (CC) tissues of control patients using RNA sequencing. A total of 78 differentially expressed circRNAs (DECs) were identified between the BA and CC tissues. The expression levels of eight circRNAs (hsa_circ_0006137, hsa_circ_0079422, hsa_circ_0007375, hsa_circ_0005597, hsa_circ_0006961, hsa_circ_0081171, hsa_circ_0084665 and hsa_circ_0075828) in the liver tissues of the BA group and control group were measured using reverse transcription-quantitative polymerase chain reaction. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis demonstrated that the identified DECs are involved in a variety of biological processes, including apoptosis and metabolism. In addition, based on the GO and KEGG pathway enrichment analyses, it was revealed that target genes that can be affected by circRNAs regulatory network were enriched in the TGF-β signaling pathway, EGFR tyrosine kinase inhibitor resistance pathway and transcription factor regulation pathway as well as other pathways that may be associated with the pathogenesis of BA. The present study revealed that circRNAs are potentially implicated in the pathogenesis of BA and could help to find promising targets and biomarkers for BA.

Biological role and regulation of circular RNA as an emerging biomarker and potential therapeutic target for cancer

Circular RNAs (circRNAs) are a unique family of endogenous RNAs devoid of 3' poly-A tails and 5' end caps. These single-stranded circRNAs, found in the cytoplasm, are synthesized via back-splicing mechanisms, merging introns, exons, or both, resulting in covalently closed circular loops. They are profusely expressed across the eukaryotic transcriptome and offer heightened stability against exonuclease RNase R compared to linear RNA counterparts. This review endeavors to provide a comprehensive overview of circRNAs' characteristics, biogenesis, and mechanisms of action. Furthermore, aimed to shed light on the potential of circRNAs as significant biomarkers in various cancer types. It has been performed an exhaustive literature review, drawing on recent studies and findings related to circRNA characteristics, synthesis, function, evaluation techniques, and their associations with oncogenesis. CircRNAs are intricately associated with tumor progression and development. Their multifaceted roles encompass gene regulation through the sponging of proteins and microRNAs, controlling transcription and splicing, interacting with RNA binding proteins (RBPs), and facilitating gene translation. Due to these varied roles, circRNAs have become a focal point in tumor pathology investigations, given their promising potential as both biomarkers and therapeutic agents. CircRNAs, due to their unique biogenesis and multifunctionality, hold immense promise in the realm of oncology. Their stability, widespread expression, and intricate involvement in gene regulation underscore their prospective utility as reliable biomarkers and therapeutic targets in cancer. As our understanding of circRNAs deepens, advanced techniques for their detection, evaluation, and manipulation will likely emerge. These advancements might catalyze the translation of circRNA-based diagnostics and therapeutics into clinical practice, potentially revolutionizing cancer care and prognosis.

DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features

Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years. However, how to make full use of multiview sequence information to predict thermostability effectively is still a challenge. In this study, we proposed a deep learning-based classifier named DeepPPThermo that fuses features of classical sequence features and deep learning representation features for classifying thermophilic and mesophilic proteins. In this model, deep neural network (DNN) and bi-long short-term memory (Bi-LSTM) are used to mine hidden features. Furthermore, local attention and global attention mechanisms give different importance to multiview features. The fused features are fed to a fully connected network classifier to distinguish thermophilic and mesophilic proteins. Our model is comprehensively compared with advanced machine learning algorithms and deep learning algorithms, proving that our model performs better. We further compare the effects of removing different features on the classification results, demonstrating the importance of each feature and the robustness of the model. Our DeepPPThermo model can be further used to explore protein diversity, identify new thermophilic proteins, and guide directed mutations of mesophilic proteins.

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.

Identification of immune-responsive circular RNAs in shrimp (Litopenaeus vannamei) upon yellow head virus infection

Circular RNAs (circRNAs) are a subclass of non-coding RNAs (ncRNAs) formed through a process known as back-splicing. They play a crucial role in the genetic regulation of various biological processes. Currently, circRNAs have been identified as participants in the antiviral response within mammalian cells. However, circRNAs in shrimp infected with the yellow head virus (YHV) remain largely unexplored. Therefore, this study aims to identify circRNAs in the hemocytes of Litopenaeus vannamei during YHV infection. We discovered 358 differentially expressed circRNAs (DECs), with 177 of them being up-regulated and 181 down-regulated. Subsequently, eight DECs, including circ_alpha-1-inhibitor 3, circ_CDC42 small effector protein 2, circ_hemicentin 2, circ_integrin alpha V, circ_kazal-type proteinase inhibitor, circ_phenoloxidase 3, circ_related protein rab-8B, and circ_protein toll-like, were randomly selected for analysis of their expression patterns during YHV infection using qRT-PCR. Furthermore, the circRNAs' characteristics were confirmed through PCR, RNase R treatment, and Sanger sequencing, all of which were consistent with the features of circRNAs. These findings contribute to a better understanding of circRNAs' involvement in the antiviral response in shrimp.

Virus-Encoded Circular RNAs: Role and Significance in Viral Infections

Circular RNAs (circRNAs) have been the focus of intense scientific research to understand their biogenesis, mechanisms of action and regulatory functions. CircRNAs are single stranded, covalently closed RNA molecules lacking the 5'-terminal cap and the 3'-terminal polyadenine chain, characteristics that make them very stable and resistant. Synthesised by both cells and viruses, in the past circRNAs were considered to have no precise function. Today, increasing evidence shows that circRNAs are ubiquitous, some of them are tissue- and cell-specific, and critical in multiple regulatory processes (i.e., infections, inflammation, oncogenesis, gene expression). Moreover, circRNAs are emerging as important biomarkers of viral infection and disease progression. In this review, we provided an updated overview of current understanding of virus-encoded and cellular-encoded circRNAs and their involvement in cellular pathways during viral infection.

Prediction of lncRNA functions using deep neural networks based on multiple networks

BACKGROUND

More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.

RESULTS

In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage.

CONCLUSIONS

DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

An integrative pipeline for circular RNA quantitative trait locus discovery with application in human T cells

MOTIVATION

Molecular quantitative trait locus (QTL) mapping has proven to be a powerful approach for prioritizing genetic regulatory variants and causal genes identified by genome-wide association studies. Recently, this success has been extended to circular RNA (circRNA), a potential group of RNAs that can serve as markers for the diagnosis, prognosis, or therapeutic targets of various human diseases. However, a well-developed computational pipeline for circRNA QTL (circQTL) discovery is still lacking.

RESULTS

We introduce an integrative method for circQTL mapping and implement it as an automated pipeline based on Nextflow, named cscQTL. The proposed method has two main advantages. Firstly, cscQTL improves the specificity by systematically combining outputs of multiple circRNA calling algorithms to obtain highly confident circRNA annotations. Secondly, cscQTL improves the sensitivity by accurately quantifying circRNA expression with the help of pseudo references. Compared to the single method approach, cscQTL effectively identifies circQTLs with an increase of 20%-100% circQTLs detected and recovered all circQTLs that are highly supported by the single method approach. We apply cscQTL to a dataset of human T cells and discover genetic variants that control the expression of 55 circRNAs. By colocalization tests, we further identify circBACH2 and circYY1AP1 as potential candidates for immune disease regulation.

AVAILABILITY AND IMPLEMENTATION

cscQTL is freely available at: https://github.com/datngu/cscQTL and https://doi.org/10.5281/zenodo.7851982.

RPIPCM: A deep network model for predicting lncRNA-protein interaction based on sequence feature encoding

LncRNA-protein interactionplays an important regulatory role in biological processes. In this paper, the proposed RPIPCM based on a novel deep network model uses the sequence feature encoding of both RNA and protein to predict lncRNA-protein interactions (LPIs). A negative sampling of sliding window method is proposed for solving the problem of unbalanced between positive and negative samples. The proposed negative sampling method is effective and helpful to solve the problem of data imbalance in the existing LPIs research by comparative experiments. Experimental results also show that the proposed sequence feature encoding method has good performance in predicting LPIs for different datasets of different sizes and types. In the RPI488 dataset related to animal, compared with the direct original sequence encoding model, the accuracy of sequence feature encoding model increased by 1.02%, the recall increased by 4.08%, and the value of MCC increased by 1.67%. In the case of the plant dataset ATH948, the sequence feature-based encoding demonstrated a 1.58% higher accuracy, a 1.53% higher recall, a 1.62% higher specificity, a 1.62% higher precision, and a 3.16% higher value of MCC compared to the direct original sequence-based encoding. Compared with the latest prediction work in the ZEA22133 dataset, RPIPCM is shown to be more effective with the accuracy increased by 2.23%, the recall increased by 1.78%, the specificity increased by 2.67%, the precision increased by 2.52%, and the value of MCC increased by 4.43%, which also proves the effectiveness and robustness of RPIPCM. In conclusion, RPIPCM of deep network model based on sequence feature encoding can automatically mine the hidden feature information of the sequence in the lncRNA-protein interaction without relying on external features or prior biomedical knowledge, and its low cost and high efficiency can provide a reference for biomedical researchers.

Identification of plant vacuole proteins by using graph neural network and contact maps

Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn .

Circular RNAs in the human brain are tailored to neuron identity and neuropsychiatric disease

Little is known about circular RNAs (circRNAs) in specific brain cells and human neuropsychiatric disease. Here, we systematically identify over 11,039 circRNAs expressed in vulnerable dopamine and pyramidal neurons laser-captured from 190 human brains and non-neuronal cells using ultra-deep, total RNA sequencing. 1526 and 3308 circRNAs are custom-tailored to the cell identity of dopamine and pyramidal neurons and enriched in synapse pathways. 29% of Parkinson's and 12% of Alzheimer's disease-associated genes produced validated circRNAs. circDNAJC6, which is transcribed from a juvenile-onset Parkinson's gene, is already dysregulated during prodromal, onset stages of common Parkinson's disease neuropathology. Globally, addiction-associated genes preferentially produce circRNAs in dopamine neurons, autism-associated genes in pyramidal neurons, and cancers in non-neuronal cells. This study shows that circular RNAs in the human brain are tailored to neuron identity and implicate circRNA-regulated synaptic specialization in neuropsychiatric diseases.

Going circular: history, present, and future of circRNAs in cancer

To date, thousands of highly abundant and conserved single-stranded RNA molecules shaped into ring structures (circRNAs) have been identified. CircRNAs are multifunctional molecules that have been shown to regulate gene expression transcriptionally and post-transcriptionally and exhibit distinct tissue- and development-specific expression patterns associated with a variety of normal and disease conditions, including cancer pathogenesis. Over the past years, due to their intrinsic stability and resistance to ribonucleases, particular attention has been drawn to their use as reliable diagnostic and prognostic biomarkers in cancer diagnosis, treatment, and prevention. However, there are some critical caveats to their utility in the clinic. Their circular shape limits their annotation and a complete functional elucidation is lacking. This makes their detection and biomedical application still challenging. Herein, we review the current knowledge of circRNA biogenesis and function, and of their involvement in tumorigenesis and potential utility in cancer-targeted therapy.

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction

Accumulating evidence suggests that circRNAs play crucial roles in human diseases. CircRNA-disease association prediction is extremely helpful in understanding pathogenesis, diagnosis, and prevention, as well as identifying relevant biomarkers. During the past few years, a large number of deep learning (DL) based methods have been proposed for predicting circRNA-disease association and achieved impressive prediction performance. However, there are two main drawbacks to these methods. The first is these methods underutilize biometric information in the data. Second, the features extracted by these methods are not outstanding to represent association characteristics between circRNAs and diseases. In this study, we developed a novel deep learning model, named iCircDA-NEAE, to predict circRNA-disease associations. In particular, we use disease semantic similarity, Gaussian interaction profile kernel, circRNA expression profile similarity, and Jaccard similarity simultaneously for the first time, and extract hidden features based on accelerated attribute network embedding (AANE) and dynamic convolutional autoencoder (DCAE). Experimental results on the circR2Disease dataset show that iCircDA-NEAE outperforms other competing methods significantly. Besides, 16 of the top 20 circRNA-disease pairs with the highest prediction scores were validated by relevant literature. Furthermore, we observe that iCircDA-NEAE can effectively predict new potential circRNA-disease associations.

Golgi_DF: Golgi proteins classification with deep forest

Introduction

Golgi is one of the components of the inner membrane system in eukaryotic cells. Its main function is to send the proteins involved in the synthesis of endoplasmic reticulum to specific parts of cells or secrete them outside cells. It can be seen that Golgi is an important organelle for eukaryotic cells to synthesize proteins. Golgi disorders can cause various neurodegenerative and genetic diseases, and the accurate classification of Golgi proteins is helpful to develop corresponding therapeutic drugs.

Methods

This paper proposed a novel Golgi proteins classification method, which is Golgi_DF with the deep forest algorithm. Firstly, the classified proteins method can be converted the vector features containing various information. Secondly, the synthetic minority oversampling technique (SMOTE) is utilized to deal with the classified samples. Next, the Light GBM method is utilized to feature reduction. Meanwhile, the features can be utilized in the penultimate dense layer. Therefore, the reconstructed features can be classified with the deep forest algorithm.

Results

In Golgi_DF, this method can be utilized to select the important features and identify Golgi proteins. Experiments show that the well-performance than the other art-of-the state methods. Golgi_DF as a standalone tools, all its source codes publicly available at https://github.com/baowz12345/golgiDF.

Discussion

Golgi_DF employed reconstructed feature to classify the Golgi proteins. Such method may achieve more available features among the UniRep features.

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.

Circular RNAs in the human brain are tailored to neuron identity and neuropsychiatric disease

Little is known about circular RNAs (circRNAs) in specific brain cells and human neuropsychiatric disease. Here, we systematically identified over 11,039 circRNAs expressed in vulnerable dopamine and pyramidal neurons laser-captured from 190 human brains and non-neuronal cells using ultra-deep, total RNA sequencing. 1,526 and 3,308 circRNAs were custom-tailored to the cell identity of dopamine and pyramidal neurons and enriched in synapse pathways. 88% of Parkinson's and 80% of Alzheimer's disease-associated genes produced circRNAs. circDNAJC6, produced from a juvenile-onset Parkinson's gene, was already dysregulated during prodromal, onset stages of common Parkinson's disease neuropathology. Globally, addiction-associated genes preferentially produced circRNAs in dopamine neurons, autism-associated genes in pyramidal neurons, and cancers in non-neuronal cells. This study shows that circular RNAs in the human brain are tailored to neuron identity and implicate circRNA- regulated synaptic specialization in neuropsychiatric diseases.

Challenges and opportunities for circRNA identification and delivery

Circular RNAs (circRNAs) are evolutionarily conserved noncoding RNAs with tissue-specific expression patterns, and exert unique cellular functions that have the potential to become biomarkers in therapeutic applications. Therefore, accurate and sensitive detection of circRNA with facile platforms is essential for better understanding of circRNA biological processes and circRNA-related disease diagnosis and prognosis; and precise regulation of circRNA through efficient delivery of circRNA or siRNA is critical for therapeutic purposes. Here, we reviewed the current development of circRNA identification methodologies, including overviewing the purification steps, summarizing the sequencing methods of circRNA, as well as comparing the advantages and disadvantages of traditional and new detection methods. Then, we discussed the delivery and manipulation strategies for circRNAs in both research and clinic treatment. Finally, the challenges and opportunities of analyzing circRNAs were addressed.

Transcriptome analysis of differentially expressed circular RNAs in the testis and ovary of golden pompano (Trachinotus blochii)

The artificial breeding of golden pompano (Trachinotus blochii) has expanded greatly in recent years, and after long-term breeding efforts, clear sexual dimorphisms have been observed in T. blochii growth traits, with females growing faster. As sponges of microRNA (miRNAs), circular RNAs (CircRNAs) can alleviate miRNA inhibition of target mRNA. However, few studies have examined sex-related CircRNAs and none of those have looked at T. blochii. To further understand the role of CircRNAs in sex differentiation and sexual size dimorphism in T. blochii, six CircRNA libraries were constructed from the testes and ovaries of T. blochii. A total of 1522 CircRNAs were found distributed over all 24 chromosomes of T. blochii. 135 differentially expressed CircRNAs (DECs) were identified by screening, These DECs were then subjected to GO enrichment, which found 47 enriched pathways. A number of CircRNAs were enriched in cellular processes and metabolic processes. According to the KEGG pathway analysis, a series of sex differentiation pathways were enriched, including the GnRH, calcium, and MAPK signaling pathways. Furthermore, we selected two CircRNAs from the DECs named circ-cacna1b and circ-octc. We found that the cacna1b gene is regulated by 7 miRNAs, 3 of which were regulated by circ-cacna1b, i.e., mmu-miR-138-5p, fru-miR-138, and pma-miR-138b. In addition, the miRNA named pma-miR-138b can regulate sex-related genes, such as sox9 and dmrt1, among others. The co-expression network of CircRNA-miRNA-mRNA showed circ-cacna1b may play a crucial role in T. blochii sex differentiation by regulating pma-miR-138b to affect the expression of sex differentiation genes. The circ-octc may be one of the largest contributors to sexual size dimorphism during growth through its effect on lipid metabolism. These findings could broaden our understanding of CircRNAs and provide new insight into their function in sex differentiation and growth.

Assessing the impacts of various factors on circular RNA reliability

Circular RNAs (circRNAs) are non-polyadenylated RNAs with a continuous loop structure characterized by a non-colinear back-splice junction (BSJ). Although millions of circRNA candidates have been identified, it remains a major challenge for determining circRNA reliability because of various types of false positives. Here, we systematically assess the impacts of numerous factors related to circRNA identification, conservation, biogenesis, and function on circRNA reliability by comparisons of circRNA expression from mock and the corresponding colinear/polyadenylated RNA-depleted datasets based on three different RNA treatment approaches. Eight important indicators of circRNA reliability are determined. The relative contribution to variability explained analyses reveal that the relative importance of these factors in affecting circRNA reliability in descending order is the conservation level of circRNA, full-length circular sequences, supporting BSJ read count, both BSJ donor and acceptor splice sites at the same colinear transcript isoforms, both BSJ donor and acceptor splice sites at the annotated exon boundaries, BSJs detected by multiple tools, supporting functional features, and both BSJ donor and acceptor splice sites undergoing alternative splicing. This study thus provides a useful guideline and an important resource for selecting high-confidence circRNAs for further investigations.

Potential roles of endothelial cells-related non-coding RNAs in cardiovascular diseases

Endothelial dysfunction is identified by a conversion of the endothelium toward decreased vasodilation and prothrombic features and is known as a primary pathogenic incident in cardiovascular diseases. An insight based on particular and promising biomarkers of endothelial dysfunction may possess vital clinical significances. Currently, non-coding RNAs due to their participation in critical cardiovascular processes like initiation and progression have gained much attention as possible diagnostic as well as prognostic biomarkers in cardiovascular diseases. Emerging line of proof has demonstrated that abnormal expression of non-coding RNAs is nearly correlated with the pathogenesis of cardiovascular diseases. In the present review, we focus on the expression and functional effects of various kinds of non-coding RNAs in cardiovascular diseases and negotiate their possible clinical implications as diagnostic or prognostic biomarkers and curative targets.

nf-core/circrna: a portable workflow for the quantification, miRNA target prediction and differential expression analysis of circular RNAs

BACKGROUND

Circular RNAs (circRNAs) are a class of covalenty closed non-coding RNAs that have garnered increased attention from the research community due to their stability, tissue-specific expression and role as transcriptional modulators via sequestration of miRNAs. Currently, multiple quantification tools capable of detecting circRNAs exist, yet none delineate circRNA-miRNA interactions, and only one employs differential expression analysis. Efforts have been made to bridge this gap by way of circRNA workflows, however these workflows are limited by both the types of analyses available and computational skills required to run them.

RESULTS

We present nf-core/circrna, a multi-functional, automated high-throughput pipeline implemented in nextflow that allows users to characterise the role of circRNAs in RNA Sequencing datasets via three analysis modules: (1) circRNA quantification, robust filtering and annotation (2) miRNA target prediction of the mature spliced sequence and (3) differential expression analysis. nf-core/circrna has been developed within the nf-core framework, ensuring robust portability across computing environments via containerisation, parallel deployment on cluster/cloud-based infrastructures, comprehensive documentation and maintenance support.

CONCLUSION

nf-core/circrna reduces the barrier to entry for researchers by providing an easy-to-use, platform-independent and scalable workflow for circRNA analyses. Source code, documentation and installation instructions are freely available at https://nf-co.re/circrna and https://github.com/nf-core/circrna .

Circ_0033596 depletion ameliorates oxidized low-density lipoprotein-induced human umbilical vein endothelial cell damage

BACKGROUND

Previous data have shown that circ_0033596 is involved in the pathogenesis of atherosclerosis (AS). The study aims to reveal the detailed mechanism of circ_0033596 in AS.

METHODS

Human umbilical vein endothelial cells (HUVECs) were treated with oxidized low-density lipoprotein (ox-LDL) to establish an AS cell model. Quantitative real-time polymerase chain reaction and western blot were implemented to detect the expression of circ_0033596, miR-637, growth factor receptor bound protein2 (GRB2), BCL2-associated x protein (Bax) and B-cell lymphoma-2 (Bcl-2). Cell viability, proliferation, apoptosis and tube formation were investigated by cell counting kit-8, EdU assay, flow cytometry and tube formation assay, respectively. The production of interleukin (IL-6) and tumor necrosis factor-α (TNF-α) was evaluated by enzyme-linked immunosorbent assay. Oxidative stress was evaluated by lipid peroxidation malondialdehyde assay kit and superoxide dismutase activity assay kit. Dual-luciferase reporter assay, RNA pull-down assay and RIP assay were performed to identify the associations among circ_0033596, miR-637 and GRB2.

RESULTS

The expression of circ_0033596 and GRB2 was significantly increased, while miR-637 was decreased in the blood of AS patients and ox-LDL-induced HUVECs compared with controls. Ox-LDL treatment inhibited HUVEC viability, proliferation and angiogenic ability and induced cell apoptosis, inflammation and oxidative stress, while these effects were attenuated after circ_0033596 knockdown. Circ_0033596 interacted with miR-637 and regulated ox-LDL-induced HUVEC damage by targeting miR-637. In addition, GRB2, a target gene of miR-637, participated in ox-LDL-induced HUVEC injury by combining with miR-637. Importantly, circ_0033596 activated GRB2 by interacting with miR-637.

CONCLUSION

Circ_0033596 depletion protected against ox-LDL-induced HUVEC injury by miR-637/GRB2 pathway, providing a therapeutic target for AS.

A review of methods for predicting DNA N6-methyladenine sites

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

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

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

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.

Transcriptome-based insights into the regulatory role of immune-responsive circular RNAs in Litopanaeus vannamei upon WSSV infection

Circular RNAs (circRNAs) are non-coding RNAs (ncRNAs) originating from a post-transcriptional modification process called back-splicing. Despite circRNAs being traditionally considered by-products rather than independently functional, circRNAs play many vital roles, such as in host immunity during viral infection. However, in shrimp, these remain largely unexplored. Therefore, this study aims to identify circRNAs in Litopenaeus vannamei in the context of WSSV infection, one of the most eradicative pathogens threatening shrimp populations worldwide. We identified 290 differentially expressed circRNAs (DECs) in L. vannamei upon WSSV infection. Eight DECs were expressed from their parental genes, including alpha-1-inhibitor-3, calpain-B, integrin-V, hemicentin-2, hemocytin, mucin-17, proPO2, and rab11-FIP4. These were examined quantitatively by qRT-PCR, which revealed the relevant expression profiles to those obtained from circRNA-Seq. Furthermore, the structural and chemical validation of the DECs conformed to the characteristics of circRNAs. One of the functional properties of circRNAs as a miRNA sponge was examined via the interaction network between DECs and WSSV-responsive miRNAs, which highlighted the targets of miRNA sponges. Our discovery could provide insight into the participation of these ncRNAs in shrimp antiviral responses.

A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins

There is a great deal of importance to SNARE proteins, and their absence from function can lead to a variety of diseases. The SNARE protein is known as a membrane fusion protein, and it is crucial for mediating vesicle fusion. The identification of SNARE proteins must therefore be conducted with an accurate method. Through extensive experiments, we have developed a model based on graph-regularized k-local hyperplane distance nearest neighbor model (GHKNN) binary classification. In this, the model uses the physicochemical property extraction method to extract protein sequence features and the SMOTE method to upsample protein sequence features. The combination achieves the most accurate performance for identifying all protein sequences. Finally, we compare the model based on GHKNN binary classification with other classifiers and measure them using four different metrics: SN, SP, ACC, and MCC. In experiments, the model performs significantly better than other classifiers.

MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances

Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease associations, and uncovering new circRNA-disease associations by wet-lab methods is time consuming and costly. Considering the limitations of existing computational methods, we propose a novel approach named MNMDCDA, which combines high-order graph convolutional networks (high-order GCNs) and deep neural networks to infer associations between circRNAs and diseases. Firstly, we computed different biological attribute information of circRNA and disease separately and used them to construct multiple multi-source similarity networks. Then, we used the high-order GCN algorithm to learn feature embedding representations with high-order mixed neighborhood information of circRNA and disease from the constructed multi-source similarity networks, respectively. Finally, the deep neural network classifier was implemented to predict associations of circRNAs with diseases. The MNMDCDA model obtained AUC scores of 95.16%, 94.53%, 89.80% and 91.83% on four benchmark datasets, i.e., CircR2Disease, CircAtlas v2.0, Circ2Disease and CircRNADisease, respectively, using the 5-fold cross-validation approach. Furthermore, 25 of the top 30 circRNA-disease pairs with the best scores of MNMDCDA in the case study were validated by recent literature. Numerous experimental results indicate that MNMDCDA can be used as an effective computational tool to predict circRNA-disease associations and can provide the most promising candidates for biological experiments.

Biogenesis and Regulatory Roles of Circular RNAs

Covalently closed, single-stranded circular RNAs can be produced from viral RNA genomes as well as from the processing of cellular housekeeping noncoding RNAs and precursor messenger RNAs. Recent transcriptomic studies have surprisingly uncovered that many protein-coding genes can be subjected to backsplicing, leading to widespread expression of a specific type of circular RNAs (circRNAs) in eukaryotic cells. Here, we discuss experimental strategies used to discover and characterize diverse circRNAs at both the genome and individual gene scales. We further highlight the current understanding of how circRNAs are generated and how the mature transcripts function. Some circRNAs act as noncoding RNAs to impact gene regulation by serving as decoys or competitors for microRNAs and proteins. Others form extensive networks of ribonucleoprotein complexes or encode functional peptides that are translated in response to certain cellular stresses. Overall, circRNAs have emerged as an important class of RNAmolecules in gene expression regulation that impact many physiological processes, including early development, immune responses, neurogenesis, and tumorigenesis.

Recent advances in targeted delivery of non-coding RNA-based therapeutics for atherosclerosis

Cardiovascular disease (CVD) has overtaken infectious illnesses as the leading cause of mortality and disability worldwide. The pathology that underpins CVD is atherosclerosis, characterized by chronic inflammation caused by the accumulation of plaques in the arteries. As our knowledge about the microenvironment of blood vessel walls deepens, there is an opportunity to fine-tune treatments to target the mechanisms driving atherosclerosis more directly. The application of non-coding RNAs (ncRNAs) as biomarkers or intervention targets is increasing. Although these ncRNAs play an important role in driving atherosclerosis and vascular dysfunction, the cellular and extracellular environments pose a challenge for targeted transmission and therapeutic regulation of ncRNAs. Specificity, delivery, and tolerance have hampered the clinical translation of ncRNA-based therapeutics. Nanomedicine is an emerging field that uses nanotechnology for targeted drug delivery and advanced imaging. Recently, nanoscale carriers have shown promising results and have introduced new possibilities for nucleic acid targeted drug delivery, particularly for atherosclerosis. In this review, we discuss the latest developments in nanoparticles to aid ncRNA-based drug development, particularly miRNA, and we analyze the current challenges in ncRNA targeted delivery. In particular, we highlight the emergence of various kinds of nanotherapeutic approaches based on ncRNAs, which can improve treatment options for atherosclerosis.

Characterization of lncRNA/circRNA-miRNA-mRNA network to reveal potential functional ceRNAs in the skeletal muscle of chicken

Skeletal muscle, comprising approximately 40% of body mass, is a highly complex and heterogeneous tissue serving a multitude of functions in the organism. Non-coding RNAs (ncRNAs) are known to participate in skeletal muscle development as critical regulators. However, the regulatory mechanisms of ncRNAs on chicken muscle traits are not well understood. In the present study, we collected the leg muscle from male embryos of Tibetan chicken at embryonic (E) 10 and E18 for RNA sequencing. A total of 6,583 differentially expressed mRNAs (DEMs) including 3,055 down-regulated and 3,528 up-regulated were identified in E18. We identified 695 differentially expressed lncRNAs (DELs) (187 down-regulated and 508 up-regulated) and 1,906 differentially expressed circRNAs (DECs) (1,224 down-regulated and 682 up-regulated) in E18. Among the 130 differentially expressed miRNAs (DEMIs), 59 were up-regulated and 71 were down-regulated in E18. Numerous DEMs and target genes for miRNAs/lncRNAs were significantly enriched in the muscle system process and cell cycle. We constructed a miRNA-gene-pathway network by considering target relationships between genes related to skeletal muscle development and miRNAs. A competing endogenous RNA (ceRNA) network was also constructed by integrating competing relationships between DEMs, DELs, and DECs. Several DELs and DECs were predicted to regulate the ADRA1B, ATP2A2, ATP2B1, CACNA1S, CACNB4, MYLK2, and ROCK2 genes. We discovered the crosstalk between the ncRNAs and their competing mRNAs, which provides insights into ceRNA function and mechanisms in the skeletal muscle development of chicken.

A pseudo-Siamese framework for circRNA-RBP binding sites prediction integrating BiLSTM and soft attention mechanism

Circular RNAs (circRNAs) are widely expressed in tissues and play a key role in diseases through interacting with RNA binding proteins (RBPs). Since the high cost of traditional technology, computational methods are developed to identify the binding sites between circRNAs and RBPs. Unfortunately, these methods suffer from the insufficient learning of features and the single classification of output. To address these limitations, we propose a novel method named circ-pSBLA which constructs a pseudo-Siamese framework integrating Bi-directional long short-term memory (BiLSTM) network and soft attention mechanism for circRNA-RBP binding sites prediction. Softmax function and CatBoost are adopted to classify, respectively, and then a pseudo-Siamese framework is constructed. circ-pSBLA combines them to get final output. To validate the effectiveness of circ-pSBLA, we compare it with other state-of-the-art methods and carry out an ablation experiment on 17 sub-datasets. Moreover, we do motif analysis on 3 sub-datasets. The results show that circ-pSBLA achieves superior performance and outperforms other methods. All supporting source codes can be downloaded from https://github.com/gyj9811/circ-pSBLA.

MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction

Circular RNA (circRNA) is closely involved in physiological and pathological processes of many diseases. Discovering the associations between circRNAs and diseases is of great significance. Due to the high-cost to verify the circRNA-disease associations by wet-lab experiments, computational approaches for predicting the associations become a promising research direction. In this paper, we propose a method, MDGF-MCEC, based on multi-view dual attention graph convolution network (GCN) with cooperative ensemble learning to predict circRNA-disease associations. First, MDGF-MCEC constructs two disease relation graphs and two circRNA relation graphs based on different similarities. Then, the relation graphs are fed into a multi-view GCN for representation learning. In order to learn high discriminative features, a dual-attention mechanism is introduced to adjust the contribution weights, at both channel level and spatial level, of different features. Based on the learned embedding features of diseases and circRNAs, nine different feature combinations between diseases and circRNAs are treated as new multi-view data. Finally, we construct a multi-view cooperative ensemble classifier to predict the associations between circRNAs and diseases. Experiments conducted on the CircR2Disease database demonstrate that the proposed MDGF-MCEC model achieves a high area under curve of 0.9744 and outperforms the state-of-the-art methods. Promising results are also obtained from experiments on the circ2Disease and circRNADisease databases. Furthermore, the predicted associated circRNAs for hepatocellular carcinoma and gastric cancer are supported by the literature. The code and dataset of this study are available at https://github.com/ABard0/MDGF-MCEC.

The circRNA-miRNA/RBP regulatory network in myocardial infarction

Myocardial infarction (MI) is a serious heart disease that causes high mortality rate worldwide. Noncoding RNAs are widely involved in the pathogenesis of MI. Circular RNAs (circRNAs) are recently validated to be crucial modulators of MI. CircRNAs are circularized RNAs with covalently closed loops, which make them stable under various conditions. CircRNAs can function by different mechanisms, such as serving as sponges of microRNAs (miRNAs) and RNA-binding proteins (RBPs), regulating mRNA transcription, and encoding peptides. Among these mechanisms, sponging miRNAs/RBPs is the main pathway. In this paper, we systematically review the current knowledge on the properties and action modes of circRNAs, elaborate on the roles of the circRNA-miRNA/RBP network in MI, and explore the value of circRNAs in MI diagnosis and clinical therapies. CircRNAs are widely involved in MI. CircRNAs have many advantages, such as stability, specificity, and wide distribution, which imply that circRNAs have a great potential to act as biomarkers for MI diagnosis and prognosis.

Genome-wide identification of circular RNAs in adult Schistosoma japonicum

Circular RNAs (circRNAs) are a class of novel, widespread, covalently closed RNAs that have played an essential role in animal gene regulation. To systematically explore circRNAs in the blood fluke Schistosoma japonicum, we performed RNA sequencing and bioinformatics analysis, and found that hundreds of circRNAs showed gender-associated expression. Among these identified circRNAs, more than 77.54% and 74.73% were putatively derived from the exon region of the genome and some circRNAs showed gender-associated expressions. The functional prediction of circRNAs (circ_003826 and circ_004690) showed potential binding sites and possibly acted as the sponge to regulate microRNAs (miRNAs) sja-miR-1, sja-miR-133 and sja-miR-3504. Altogether, these findings demonstrated that S. japonicum also contains circRNAs, which may have potential regulatory roles during schistosome development.

Comprehensive Analysis of circRNA-miRNA-mRNA Regulatory Network and Novel Potential Biomarkers in Acute Myocardial Infarction

Background

Circular RNA (circRNA) plays an important role in the regulation of gene expression and the occurrence of human diseases. However, studies on the role of circRNA in acute myocardial infarction (AMI) are limited. This study was performed to explore novel circRNA-related regulatory networks in AMI, aiming to better understand the molecular mechanism of circRNAs involvement in AMI and provide basis for further scientific research and clinical decision-making.

Methods

The AMI-related microarray datasets GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), and GSE24519 (mRNA) were obtained from the Gene Expression Omnibus (GEO) database. After differential expression analysis, the regulatory relationships between these DERNAs were identified by online databases circBank, circInteractome, miRDB, miRWalk, Targetscan, and then two circRNA-miRNA-mRNA regulatory networks were constructed. Differentially expressed genes (DEGs) in this network were selected followed by enrichment analysis and protein–protein interaction (PPI) analysis. Hub genes were identified using Cytohubba plug-in of Cytoscape software. Hub genes and hub gene-related miRNAs were used for receiver operating characteristic curve (ROC) analysis to identify potential biomarkers. The relative expression levels of these biomarkers were further assessed by GSE31568 (miRNA) and GSE66360 (mRNA). Finally, on the basis of the above analysis, myocardial hypoxia model was constructed to verify the expression of Hub genes and related circRNAs.

Results

A total of 83 DEcircRNAs, 109 CoDEmiRNAs and 1204 DEGs were significantly differentially expressed in these datasets. The up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively. These circRNA-related DEGs were mainly enriched in the terms of “FOXO signaling pathway,” “T cell receptor signaling pathway,” “MAPK signaling pathway,” “Insulin resistance,” “cAMP signaling pathway,” and “mTOR signaling pathway.” The top 10 hub genes ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1 were identified from the PPI network. Hub genes GPM6A, SRSF1, ANK2 and hub gene-related circRNAs hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, and hsa_circ_0045519 were identified as potential biomarkers in AMI.

Conclusion

In this study, the potential circRNAs associated with AMI were identified and two circRNA-miRNA-mRNA regulatory networks were constructed. This study explored the mechanism of circRNA involvement in AMI and provided new clues for the selection of new diagnostic markers and therapeutic targets for AMI.