Bioinformatics Analysis of Circular RNA Sequencing: Introduction, Workflow, and Analysis Contents

Bioinformatics Analysis of Circular RNA Sequencing: Introduction, Workflow, and Analysis Contents

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CircRNA is a new type of non-coding RNA (ncRNA) produced by non-sequential exon, intron, or both backsplicing. They are distinguished by their covalently closed loop feature, which means they lack 5' end caps and 3' poly-A tails. Because of their nuclease resistance, CircRNAs are extremely reliable types of ncRNAs. CircRNAs were initially thought to be viral genomes or byproducts of mis-splicing events when they were discovered in the 1970s. However, a recent study has confirmed that circRNAs have essential biological mechanisms and are evolutionarily preserved across plants, animals, and humans.

CircRNAs have the potential to behave as miRNA sponges and regulate gene expression. Surprisingly, a wide variety of circRNAs are abnormally exhibited disease contexts, implying that they are linked to the onset and progression of human diseases. CircRNAs also serve as frameworks for translation, regulators of substitute splicing and gene expression, and scaffolds for the arrangement of protein complexes in cellular mechanisms, and modulators of rRNA and tRNA biogenesis. Due to their involvement in immune regulation and viral infection, they can also be utilized to enhance immune activation for antiviral therapeutic purposes.

The identification of circRNAs from high-throughput transcriptome data is the first and most important step in understanding their biogenesis and function. In a variety of microbes, high-throughput sequencing of rRNA/linear RNA-depleted RNA combined with computational equipment has resulted in the identification of thousands of new circRNAs and quantitative assessments of their linear host transcripts. CircRNAs, unlike miRNAs and other small RNAs, are difficult to distinguish from other RNA species due to their size or electrophoretic mobility. As a result, they are commonly found in rRNA-depleted libraries and are enriched in libraries handled with RNase R, which only digests linear RNA.

Experiment workflow and data analysis flowchart for circRNA analysis. (Guo, 2018)Figure 1. Experiment workflow and data analysis flowchart for circRNA analysis. (Guo, 2018)

Advantages and CircRNA Sequencing Workflow

The following are some of the benefits of circular RNA sequencing: (1) circRNAs, both known and unknown, are identified. (2) enables circRNA profiling over a broad dynamic range, and (3) investigates novel biomarkers and circRNA regulatory systems.

The following is a general circRNA sequencing workflow. Depletion of rRNA is the first process in creating a circRNA sequencing library, preceded by linear RNA digestion and strand-specific library preparation. Quality management is carried out by our highly experienced expert team, who follow all procedures to ensure accurate and unbiased outcomes.

Circular RNA Data Analysis Content

Six procedures are involved in the standard data assessment of circular RNA sequencing data. The implementation of basic statistics is the first process. To get clean data, the connector sequence and low-quality sequence are excluded in this process. To erase ribosomal RNA data, the sequence is evaluated to a ribosomal database. The prediction and recognition of circular RNA is the second process. The next process is linear gene annotation, which pinpoints the location of circular RNA. The fourth process entails determining the location of circular RNA on the genome. Following that, a quantitative assessment of circular RNA expression will be performed. The assessment of expression differences between specimens is the final step (between groups).

Gene GO function and differential circular enrichment assessments are being more comprehensive information assessments. Pathway function and gene enrichment assessment using differential RNA are also included. Finally, interaction assessment of circular RNA and miRNA, which is appropriate to organisms with an mRNA database, is feasible.

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  1. Wang D, Luo Y, Wang G, Yang Q. Circular RNA expression profiles and bioinformatics analysis in ovarian endometriosis. Molecular genetics & genomic medicine. 2019, 7(7)
  2. Jakobi T, Dieterich C. Computational approaches for circular RNA analysis. Wiley Interdisciplinary Reviews: RNA. 2019, 10(3).
  3. Guo S, Xu X, Ouyang Y, et al. Microarray expression profile analysis of circular RNAs in pancreatic cancer. Molecular medicine reports. 2018, 17(6).
  4. Hansen TB, Venø MT, Damgaard CK, Kjems J. Comparison of circular RNA prediction tools. Nucleic acids research. 2016, 44(6).
* For Research Use Only. Not for use in diagnostic procedures.
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