What is Circular RNA?
Circular RNA, formed by alternative splicing in the process of transcription, is a special class of non-coding RNA molecules. The circular RNA differs from the traditional linear RNA, as the circular RNA molecule has a closed ring structure and is not affected by RNA exonuclease, so its expression is more stable and will not easily degrade. Accurate circular RNA identification, source gene analysis, circular RNA and miRNA sponge effect analysis of samples with reference genomes can be applied to the construction of circular RNA expression profiles of species, the development of disease biomarkers, the study of action mechanism and so on. At present, circular RNA sequencing analysis is more widely used in the fields of medicine and agronomy.
Application of Circular RNA Analysis
CircRNAs are a class of non-coding RNAs that form closed continuous loops, distinguishing them from linear RNAs. They are increasingly recognized for their involvement in gene regulation, disease mechanisms, and potential therapeutic applications.
Gene Regulation
CircRNAs function as microRNA (miRNA) sponges, sequestering miRNAs and thereby modulating gene expression at the post-transcriptional level.
Disease Biomarkers
Stable in body fluids such as blood and saliva, CircRNAs display altered expression patterns in diseases like cancer, cardiovascular disorders, and neurological conditions, suggesting their potential as biomarkers.
Functional Studies
Advances in RNA sequencing and bioinformatics facilitate the identification and characterization of CircRNAs. Functional studies involve manipulating CircRNA levels in cell models through knockdown/overexpression techniques or deleting CircRNA loci using CRISPR/Cas9, revealing their roles in cellular processes.
Therapeutic Targets
Due to their unique properties, CircRNAs are promising targets for therapeutic intervention. Small molecules or antisense oligonucleotides can modulate CircRNA activity, offering new avenues for treatment. Targeting oncogenic CircRNAs, for instance, holds potential in suppressing tumor growth in cancer therapy.
CD Genomics Data Analysis Pipeline
Sample Submission Guidelines of Sequencing
| I. Standard Information Analysis | 1. Basic Statistics 1.1 Remove the connector sequence and low-quality sequence to get clean data; 1.2 Compare with ribosomal database to remove ribosomal RNA data; |
| 2. Circular RNA prediction and identification; | |
| 3. Linear gene annotation where circular RNA is located; | |
| 4. Location of circular RNA on the genome; | |
| 5. Quantitative analysis of circular RNA expression; | |
| 6. Analysis of expression differences between samples (between groups); | |
| II. Advanced Information Analysis | 1. Gene GO function and enrichment analysis of differential circular RNA sources; |
| 2. Pathway function and enrichment analysis of genes derived from differential circular RNA; | |
| 3. Interaction analysis of circular RNA and miRNA (applicable to the species that with mRNA database) |
What Are the Advantages of Our Services?
Advanced RNA Sequencing Capabilities
We utilize advanced RNA sequencing to thoroughly analyze circRNAs in diverse samples, enabling precise identification and differential expression analysis across various conditions.
Customized Bioinformatics Solutions
Our bioinformatics team excels in crafting tailored solutions for circRNA analysis, utilizing advanced algorithms to annotate circRNA structures, forecast miRNA interactions, and uncover regulatory networks, enhancing insights into gene regulation for potential diagnostic and therapeutic applications.
Functional Validation Strategies
We provide advanced methods for validating circRNAs, using techniques like knockdown/overexpression and CRISPR/Cas9 editing to uncover their roles in diseases.
Integration of Multi-Omics Data
Incorporating multi-omics, we integrate transcriptomic, genomic, and epigenomic data to reveal circRNA roles in disease, development, and cellular signaling.
Rapid Turnaround Time and Reliable Support
We ensure efficient, high-quality data with rapid turnaround and expert support from consultation to interpretation for seamless circRNA study collaboration.
With extensive expertise in multi-omics integrated analysis
We excel in delivering an extensive array of advanced sequencing services. Our specialization encompasses, but is not limited to, integrated Genomics and Metabolomics Analysis, Genomics and Transcriptomics Analysis, Microbiomics and Metabolomics Analysis, Proteomics and Transcriptomics Analysis, and Transcriptomics and Metabolomics Analysis. Our professional acumen is dedicated to effectively addressing and resolving your research challenges.
How It Works
CD Genomics is a high-tech company specializing in multiomic data analysis. We provide services such as project design, data analysis, and database construction. With a focus on developing breakthrough products and services, we are a pioneer in the biotechnology industry, serving researchers and partners worldwide.
Let Us Help You
Unearthing key information from the complicated circular RNA sequencing data is the main reason for doing circular RNA research. The standard bioinformatic analysis process is not enough to discover all the important information in the data. Our bioinformatics team will not only provide customers with standardized analysis content, advanced analysis content that has been rigorously tested and evaluated, but can also provide a full range of personalized customized analysis services in accordance with each customer's project needs. If you are interested in our services, please contact us for more detailed information. Our representative is ready to answer your questions and get a complete understanding of your needs.
What Does Analysis of Circular RNA Show?
Quality Control
Alignment Analysis
The X-axis shows the length of the chromosomes, and the Y-axis indicates the log2 of the median of read density. Blue and green indicate the positive and negative strand respectively.
Statistics of CircRNA
Pie chart of circRNA classification for sample
CircRNA Expression Quantitative Analysis
Boxplot of TPM for each sample. The x-axis shows the sample names and the y-axis shows the log10(TPM). Each box has five statistical magnitudes (max value, upper quartile, median, lower quartile and min value).
TPM density distribution. The x-axis shows the log10(TPM) and the y-axis shows density. Different colors represent different samples.
Correlation analysis between samples. The scatter diagrams demonstrate the correlation coefficient between samples.
Differential Expression Analysis for CircRNA
The expression of differential circRNA is visualized by volcano plot. The Volcano plot provides a way to perform a quick visual identification of the circRNA displaying large-magnitude changes which are also statistically significant. The plot is constructed by plotting the FDR (-log10) on the y-axis, and the expression fold change (log2) between the two experimental groups on the x-axis. There are two regions of interest in the plot: those points that are found towards the top of the plot (high statistical significance) and at the extreme left or right (strongly down and up-regulated respectively).
Cluster analysis of differentially expressed circRNA. Hierarchical clustering is based on TPMs, where log2(TPM) is used for clustering. Red color represents circRNA with higher expression, while green color represents circRNA with lower expression. Different columns represent different samples, while different rows represent different circRNA.
Functional Annotation
Gene ontology (GO - Gene Ontology Consortium, 2000) enrichment analysis is a set of the internationally standardized classification system of gene function description that attempts to identify GO terms that are significantly associated with differentially expressed protein coding genes. GO molecules are divided into three main categories: 1) Cellular Component: used to describe the subcellular structure, location and macromolecular complexes, such as nucleoli, telomere and recognition of the initial complex; 2) Molecular Function: used to describe the gene, gene products, individual functions, such as carbohydrate binding or ATP hydrolase activity; 3) Biological Process: used to describe the products encoded by genes involved in biological processes, such as mitosis or purine metabolism.
KEGG is called Kyoto Encyclopedia of Genes and Genomes, it is the main public database of the pathways. A systematic analysis of the metabolic pathways of gene products and compounds in cells and the database of the function of these gene products (KEGG PATHWAY), drug (KEGG DRUG), disease (KEGG DISEASE), functional model (KEGG MODULE), gene sequence (KEGG GENES) and the genome of the genome (KEGG GENOME) and so on. The KO (KEGG ORTHOLOG) system links the various KEGG annotation systems, and KEGG has developed a complete KO annotation system to annotate genomic or transcriptome functionalities of newly sequenced species.
The scatter plot is a graphical representation of the KEGG enrichment analysis results. In this figure, the degree of KEGG enrichment is measured by rich factor, qvalue, and the number of genes enriched in this pathway. Wherein the rich factor refers to the ratio of the number of differentially expressed genes located in the pathway to the total number of annotated genes located in the pathway. The larger the rich factor, the greater the degree of enrichment. Qvalue is the corrected pvalue after the multiple hypothesis test. The value range of qvalue is [0,1], the closer to zero, the more significant the enrichment is.
Title: Circular DNA tumor viruses make circular RNAs
Publication: Proc Natl Acad Sci U S A.
Main Methods: circRNA Sequencing, Bioinformatic Analysis
For circRNA sequencing, ribosome-depleted and RNase R-treated RNA samples were used for library preparation and subsequently sequenced using the Illumina HiSeq platform in PE150 sequencing mode (CD Genomics). The accession number for the sequencing data reported in this paper is Gene Expression Omnibus database GSE117798. Contact us to request our Circular RNA Analysis data report.
Abstract:
Epstein-Barr virus (EBV) and Kaposi's sarcoma herpesvirus (KSHV) produce stable circular RNAs (circRNAs) that may contribute to oncogenesis. EBV generates abundant circBARTs from the BART locus in all latency types except the B95-8 strain. KSHV-infected cells produce circRNAs from the vIRF4 locus and PAN RNA locus, suggesting a novel hyperbacksplicing mechanism. In coinfected cells, EBV circBARTs, KSHV circvIRF4, and circPANs localize in different cellular fractions, indicating distinct functional roles.
Main Research Results:
EBV circRNA Sequencing: RNA sequencing of EBV-negative (PTLD4, PTLD5) and EBV-positive PTLD (PTLD6, PTLD9) samples revealed two circRNA backspliced junctions (BSJs) from the BART locus in EBV-positive samples: BSJ1 and BSJ2. BSJ1 fuses exon IV's 3′ end with exon II's 5′ end, and BSJ2 fuses exon IV's 3′ end with exon IIIa's 5′ end. These BSJs were confirmed in RNase R-treated RNA from EBV-KSHV coinfected BC1 cells, supporting backsplicing events.
Characterization of EBV circBARTs in EBV Cell Lines: BSJ1 and BSJ2 junction reads of circBART_1 and circBART_2 were identified at high levels in EBV-positive PTLDs and latent BC1 cells. RT-PCR with junction-spanning primers confirmed these circRNAs in various cell lines. CircBART_1.1 and circBART_1.2 include exons II, IIIa, IIIb, and IV, while circBART_2.1 and circBART_2.2 lack exon II. Expression was found in multiple EBV latency forms but absent in the B95-8 cell line with a BART locus deletion.
Fig. 1. Identification and validation of EBV circRNAs. (A) Comparison of poly(A)+ and RNase R-treated RNA from EBV-positive PTLD9 identified RNase R-resistant circRNAs, with a high concentration in the BART region. (B) BSJ1 and BSJ2 configurations in BART circRNAs were mapped, excluding miRNA regions. (C) RT-PCR identified four circBART isoforms in EBV-positive cell lines: circBART_1.1, circBART_2.1, circBART_1.2, and circBART_2.2. (D) RNase R-treated RNAs confirmed circBART expression in various EBV latency types, with negative controls validating specificity.
KSHV circRNAs Sequencing: RNA sequencing of KSHV-infected PEL cell lines BCBL1 and BC-1 identified numerous circRNAs, notably circvIRF4 and circPAN/K7.3. CircvIRF4, an intronic-exonic circRNA, was abundant in latent cells but decreased upon viral reactivation. CircPAN/K7.3 circRNAs were highly expressed, especially after reactivation. Validation with RT-PCR confirmed multiple circRNAs in different samples, including KS tumors and MCD. An RNase H assay verified the circularity of circvIRF4, demonstrating its resistance to RNase R digestion.
Fig. 2. Identification and validation of KSHV circRNAs. (A) Comparison of poly(A)+ and RNase R-treated RNA from KSHV-infected BCBL1 cells identified RNase R-resistant circRNAs, including circvIRF4 and circPAN/K7.3, with specific BSJs mapped to viral genome regions. (B) RT-PCR validated circvIRF4 and circPAN/K7.3 in KSHV-positive PEL cell lines, showing differential expression upon viral activation. (C) Detection of circvIRF4 and circPAN/K7.3 in KS patient tissues, correlating with KSHV infection markers. (D) In vitro RNase H assays confirmed the circularity of circvIRF4, distinguishing it from linear transcripts.
Conclusions:
This study explores RNA circularization in human gammaherpesviruses, highlighting potential roles in virus maintenance and tumorigenesis. Viral circRNAs, expressed from defined operons, offer insights into RNA biology and may provide targets for future therapeutic strategies.
1. What Does Circular RNA do?
CircRNA plays a regulating role in gene expression, and an essential role in the process of biological development, such as miRNA sponges, endogenous RNAs, and biomarkers, as well as a critical role in the diagnosis of diseases.
2. What is Circular RNA Sequencing?
Circular RNA sequencing (circRNA-seq) is a specialized form of RNA sequencing designed to identify and characterize circular RNAs (circRNAs), which are a type of non-coding RNA that forms a covalently closed continuous loop. This type of sequencing provides insights into the abundance, structure, and function of circRNAs within a given sample.
3. What are the Key Steps of Circular RNA Sequencing?
Circular RNA sequencing involves RNA extraction, RNase R treatment to enrich circRNAs, library preparation, high-throughput sequencing, data analysis with tools like CIRI, and validation using RT-PCR, Northern blotting, or additional RNase R treatment.
4. What are the Advantages of Circular RNA Sequencing?
Circular RNA sequencing enhances circRNA detection, enables comprehensive profiling, and provides novel insights into RNA biology and gene regulation, advancing understanding and offering the potential for diagnostic and therapeutic development.
5. How is the Circular RNA Sequencing Data Analysis?
Circular RNA sequencing data analysis involves quality control, alignment to the reference genome, and detection of back-splicing junctions using tools like CIRCexplorer2, find_circ, and DCC, followed by quantification with CIRIquant and normalization methods such as TPM or RPKM for accurate expression level assessment.
