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Circular RNA Analysis

Circular RNA Analysis

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As one of the circular RNA sequencing data analysis service providers, CD Genomics can help you uncover the secret of circular RNA in organisms using our bioinformatic analysis technology. Our unique skills in data analysis can meet customers' needs in personalized data analysis and provide the comprehensive data analysis results.

Introduction of 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.

CD Genomics Data Analysis Pipeline

Circular RNA Analysis
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)

Circular RNA Analysis Demo Results

Quality Control

Quality Control

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.

Alignment Analysis

Pie chart of circRNA classification for samplePie chart of circRNA classification for sample

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).

CircRNA Expression Quantitative Analysis

TPM density distribution. The x-axis shows the log10(TPM) and the y-axis shows density. Different colors represent different samplesTPM 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 samplesCorrelation 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).

Differential Expression Analysis for CircRNA

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

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.

Functional Annotation

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.

Functional Annotation

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.

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.

How It Works

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.

* For Research Use Only. Not for use in diagnostic procedures.
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