CircRNAprofiler: an R-based computational framework for the downstream analysis of circular RNAs.
Ontology highlight
ABSTRACT:
SUBMITTER: Aufiero S
PROVIDER: S-EPMC7191743 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
circRNAprofiler: an R-based computational framework for the downstream analysis of circular RNAs.
Aufiero Simona S Reckman Yolan J YJ Tijsen Anke J AJ Pinto Yigal M YM Creemers Esther E EE
BMC bioinformatics 20200429 1
<h4>Background</h4>Circular RNAs (circRNAs) are a newly appreciated class of non-coding RNA molecules. Numerous tools have been developed for the detection of circRNAs, however computational tools to perform downstream functional analysis of circRNAs are scarce.<h4>Results</h4>We present circRNAprofiler, an R-based computational framework that runs after circRNAs have been identified. It allows to combine circRNAs detected by multiple publicly available annotation-based circRNA detection tools a ...[more]
Similar Datasets
Project description:MotivationCircular RNAs (circRNAs), a class of non-coding RNAs generated from non-canonical back-splicing events, have emerged to play key roles in many biological processes. Though numerous tools have been developed to detect circRNAs from rRNA-depleted RNA-seq data based on back-splicing junction-spanning reads, computational tools to identify critical genomic features regulating circRNA biogenesis are still lacking. In addition, rigorous statistical methods to perform differential expression (DE) analysis of circRNAs remain under-developed.ResultsWe present circMeta, a unified computational framework for circRNA analyses. circMeta has three primary functional modules: (i) a pipeline for comprehensive genomic feature annotation related to circRNA biogenesis, including length of introns flanking circularized exons, repetitive elements such as Alu elements and SINEs, competition score for forming circulation and RNA editing in back-splicing flanking introns; (ii) a two-stage DE approach of circRNAs based on circular junction reads to quantitatively compare circRNA levels and (iii) a Bayesian hierarchical model for DE analysis of circRNAs based on the ratio of circular reads to linear reads in back-splicing sites to study spatial and temporal regulation of circRNA production. Both proposed DE methods without and with considering host genes outperform existing methods by obtaining better control of false discovery rate and comparable statistical power. Moreover, the identified DE circRNAs by the proposed two-stage DE approach display potential biological functions in Gene Ontology and circRNA-miRNA-mRNA networks that are not able to be detected using existing mRNA DE methods. Furthermore, top DE circRNAs have been further validated by RT-qPCR using divergent primers spanning back-splicing junctions.Availability and implementationThe software circMeta is freely available at https://github.com/lichen-lab/circMeta.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7999137 | biostudies-literature
Project description:Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer's disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.
| S-EPMC7450300 | biostudies-literature
Project description:Circular RNAs (circRNAs) are a class of single-stranded, covalently closed RNA molecules with a variety of biological functions. Studies have shown that circRNAs are involved in a variety of biological processes and play an important role in the development of various complex diseases, so the identification of circRNA-disease associations would contribute to the diagnosis and treatment of diseases. In this review, we summarize the discovery, classifications and functions of circRNAs and introduce four important diseases associated with circRNAs. Then, we list some significant and publicly accessible databases containing comprehensive annotation resources of circRNAs and experimentally validated circRNA-disease associations. Next, we introduce some state-of-the-art computational models for predicting novel circRNA-disease associations and divide them into two categories, namely network algorithm-based and machine learning-based models. Subsequently, several evaluation methods of prediction performance of these computational models are summarized. Finally, we analyze the advantages and disadvantages of different types of computational models and provide some suggestions to promote the development of circRNA-disease association identification from the perspective of the construction of new computational models and the accumulation of circRNA-related data.
| S-EPMC8575014 | biostudies-literature
Project description:Replicating circular RNAs are independent plant pathogens known as viroids, or act to modulate the pathogenesis of plant and animal viruses as their satellite RNAs. The rate of discovery of these subviral pathogens was low over the past 40 years because the classical approaches are technical demanding and time-consuming. We previously described an approach for homology-independent discovery of replicating circular RNAs by analysing the total small RNA populations from samples of diseased tissues with a computational program known as progressive filtering of overlapping small RNAs (PFOR). However, PFOR written in PERL language is extremely slow and is unable to discover those subviral pathogens that do not trigger in vivo accumulation of extensively overlapping small RNAs. Moreover, PFOR is yet to identify a new viroid capable of initiating independent infection. Here we report the development of PFOR2 that adopted parallel programming in the C++ language and was 3 to 8 times faster than PFOR. A new computational program was further developed and incorporated into PFOR2 to allow the identification of circular RNAs by deep sequencing of long RNAs instead of small RNAs. PFOR2 analysis of the small RNA libraries from grapevine and apple plants led to the discovery of Grapevine latent viroid (GLVd) and Apple hammerhead viroid-like RNA (AHVd-like RNA), respectively. GLVd was proposed as a new species in the genus Apscaviroid, because it contained the typical structural elements found in this group of viroids and initiated independent infection in grapevine seedlings. AHVd-like RNA encoded a biologically active hammerhead ribozyme in both polarities, and was not specifically associated with any of the viruses found in apple plants. We propose that these computational algorithms have the potential to discover novel circular RNAs in plants, invertebrates and vertebrates regardless of whether they replicate and/or induce the in vivo accumulation of small RNAs.
| S-EPMC4263765 | biostudies-literature
Project description:Small silencing RNAs, including microRNAs, endogenous small interfering RNAs (endo-siRNAs) and Piwi-interacting RNAs (piRNAs), have been shown to play important roles in fine-tuning gene expression, defending virus and controlling transposons. Loss of small silencing RNAs or components in their pathways often leads to severe developmental defects, including lethality and sterility. Recently, non-templated addition of nucleotides to the 3' end, namely tailing, was found to associate with the processing and stability of small silencing RNAs. Next Generation Sequencing has made it possible to detect such modifications at nucleotide resolution in an unprecedented throughput. Unfortunately, detecting such events from millions of short reads confounded by sequencing errors and RNA editing is still a tricky problem. Here, we developed a computational framework, Tailor, driven by an efficient and accurate aligner specifically designed for capturing the tailing events directly from the alignments without extensive post-processing. The performance of Tailor was fully tested and compared favorably with other general-purpose aligners using both simulated and real datasets for tailing analysis. Moreover, to show the broad utility of Tailor, we used Tailor to reanalyze published datasets and revealed novel findings worth further experimental validation. The source code and the executable binaries are freely available at https://github.com/jhhung/Tailor.
| S-EPMC4632877 | biostudies-other
Project description:BACKGROUND:Heart failure is a leading cause of human morbidity and mortality. Circular RNAs (circRNAs) are a newly discovered class of RNA that have been found to have important physiological and pathological roles. In the current study, we de novo analyzed existing whole transcriptome data from 5 normal and 5 dilated cardiomyopathy (DCM) human heart samples and compared the results with circRNAs that have been previously reported in human, mouse and rat hearts. RESULTS:Our analysis identifies a list of cardiac circRNAs that are reliably detected in multiple studies. We have also defined the top 30 most abundant circRNAs in healthy human hearts which include some with previously unrecognized cardiac roles such as circHIPK3_11 and circTULP4_1. We further found that many circRNAs are dysregulated in DCM, particularly transcripts originating from DCM-related gene loci, such as TTN and RYR2. In addition, we predict the potential of cardiac circRNAs to sponge miRNAs that have reported roles in heart disease. We found that circALMS1_6 has the highest potential to bind miR-133, a microRNA that can regulate cardiac remodeling. Interestingly, we detected a novel class of circRNAs, referred to as read-though (rt)-circRNAs which are produced from exons of two different neighboring genes. Specifically, rt-circRNAs from SCAF8 and TIAM2 were observed to be dysregulated in DCM and these rt-circRNAs have the potential to sponge multiple heart disease-related miRNAs. CONCLUSIONS:In summary, this study provides a valuable resource for exploring the function of circRNAs in human heart disease and establishes a functional paradigm for identifying novel circRNAs in other tissues.
| S-EPMC7648966 | biostudies-literature
Project description:BACKGROUND:The studies of functions of circular RNAs (circRNAs) are heavily focused on the regulation of gene expression through interactions with multiple miRNAs. However, the number of predicted target genes is typically overwhelming due to the synergistic complexity caused by two factors ? the binding of multiple miRNAs to a circRNA and the existence of multiple targets for each miRNA. Analysis of common targets (ACT) was designed to facilitate the identification of potential circRNA targets. RESULTS:We demonstrated the feasibility of the proposed feature/measurement to assess which genes are more likely to be regulated by circRNAs with given sequences by calculating the level of co-regulation by multiple miRNAs. The web service is made freely available at http://lab-x-omics.nchu.edu.tw/ACT_Server . CONCLUSIONS:ACT allows users to identify potential circRNA-regulated genes and their associated pathways for further investigation.
| S-EPMC6607602 | biostudies-literature
Project description:As a major class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been implicated in various critical biological processes. Accumulating researches have linked dysregulations and mutations of lncRNAs to a variety of human disorders and diseases. However, to date, only a few human lncRNAs have been associated with diseases. Therefore, it is very important to develop a computational method to globally predict potential associated diseases for human lncRNAs. In this paper, we developed a computational framework to accomplish this by combining human lncRNA expression profiles, gene expression profiles, and human disease-associated gene data. Applying this framework to available human long intergenic noncoding RNAs (lincRNAs) expression data, we showed that the framework has reliable accuracy. As a result, for non-tissue-specific lincRNAs, the AUC of our algorithm is 0.7645, and the prediction accuracy is about 89%. This study will be helpful for identifying novel lncRNAs for human diseases, which will help in understanding the roles of lncRNAs in human diseases and facilitate treatment. The corresponding codes for our method and the predicted results are all available at http://asdcd.amss.ac.cn/MingXiLiu/lncRNA-disease.html.
| S-EPMC3879311 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is currently still a major factor leading to death, lacking of reliable biomarkers. Therefore, deep understanding the pathogenesis for HCC is of great importance. The emergence of circular RNA (circRNA) provides a new way to study the pathogenesis of human disease. Here, we employed the prediction tool to identify circRNAs based on RNA-seq data. Then, to investigate the biological function of the circRNA, the candidate circRNAs were associated with the protein-coding genes (PCGs) by GREAT. We found significant candidate circRNAs expression alterations between normal and tumor samples. Additionally, the PCGs associated with these candidate circRNAs were also found have discriminative expression patterns between normal and tumor samples. The enrichment analysis illustrated that these PCGs were predominantly enriched for liver/cardiovascular-related diseases such as atherosclerosis, myocardial ischemia and coronary heart disease, and participated in various metabolic processes. Together, a further network analysis indicated that these PCGs play important roles in the regulatory and the PPI network. Finally, we built a classification model to distinguish normal and tumor samples by using candidate circRNAs and their associated genes, respectively. Both of them obtained satisfactory results (~ 0.99 of AUC for circRNA and PCG). Our findings suggested that the circRNA could be a critical factor in HCC, providing a useful resource to explore the pathogenesis of HCC.
| S-EPMC5367815 | biostudies-literature
Project description:Recent studies have identified circular RNAs (circRNAs) expressed from the Epstein-Barr virus (EBV) and Kaposi's sarcoma herpesvirus (KSHV) human DNA tumor viruses. To gain initial insights into the potential relevance of EBV circRNAs in virus biology and disease, we assessed the circRNAome of the interspecies homologue rhesus macaque lymphocryptovirus (rLCV) in a naturally occurring lymphoma from a simian immunodeficiency virus (SIV)-infected rhesus macaque. This analysis revealed rLCV orthologues of the latency-associated EBV circular RNAs circRPMS1_E4_E3a and circEBNA_U. Also identified in two samples displaying unusually high lytic gene expression was a novel rLCV circRNA that contains both conserved and rLCV-specific RPMS1 exons and whose backsplice junctions flank an rLCV lytic origin of replication (OriLyt). Analysis of a lytic infection model for the murid herpesvirus 68 (MHV68) rhadinovirus identified a cluster of circRNAs near an MHV68 lytic origin of replication, with the most abundant of these, circM11_ORF69, spanning the OriLyt. Lastly, analysis of KSHV latency and reactivation models revealed the latency associated circRNA originating from the vIRF4 gene as the predominant viral circRNA. Together, the results of this study broaden our appreciation for circRNA repertoires in the Lymphocryptovirus and Rhadinovirus genera of gammaherpesviruses and provide evolutionary support for viral circRNA functions in latency and viral replication.IMPORTANCE Infection with oncogenic gammaherpesviruses leads to long-term viral persistence through a dynamic interplay between the virus and the host immune system. Critical for remodeling of the host cell environment after the immune responses are viral noncoding RNAs that modulate host signaling pathways without attracting adaptive immune recognition. Despite the importance of noncoding RNAs in persistent infection, the circRNA class of noncoding RNAs has only recently been identified in gammaherpesviruses. Accordingly, their roles in virus infection and associated oncogenesis are unknown. Here we report evolutionary conservation of EBV-encoded circRNAs determined by assessing the circRNAome in rLCV-infected lymphomas from an SIV-infected rhesus macaque, and we report latent and lytic circRNAs from KSHV and MHV68. These experiments demonstrate utilization of the circular RNA class of RNAs across 4 members of the gammaherpesvirus subfamily, and they identify orthologues and potential homoplastic circRNAs, implying conserved circRNA functions in virus biology and associated malignancies.
| S-EPMC6401440 | biostudies-literature