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Updated: Oct 1, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Evaluation of CircRNA Sequence Assembly Methods Using Long Reads.

Jingjing Zhang^1,2, Md Tofazzal Hossain^1,2, Weiguo Liu³

¹University of Chinese Academy of Sciences, Beijing, China.

Frontiers in Genetics

|March 3, 2022

Summary

This summary is machine-generated.

Accurate circular RNA (circRNA) sequencing requires combining multiple short-read assembly algorithms. Long-read sequencing data helps evaluate and improve circRNA assembly accuracy for reliable functional studies.

Keywords:

assembly circRNA full-length sequences long reads short reads

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Last Updated: Oct 1, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Use of Alu Element Containing Minigenes to Analyze Circular RNAs

Use of Alu Element Containing Minigenes to Analyze Circular RNAs

Published on: March 10, 2020

Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

Circular RNAs (circRNAs) are increasingly studied for their roles in gene regulation and disease.
Accurate full-length circRNA sequences are crucial for functional studies.
Existing short-read assembly methods struggle with linear transcripts, impacting circRNA identification.

Purpose of the Study:

To evaluate the performance of short-read circRNA assembly algorithms using long-read sequencing data.
To identify optimal strategies for reliable full-length circRNA sequence reconstruction.
To introduce a screening protocol for improving circRNA assembly accuracy.

Main Methods:

Utilized long-read sequencing data (Nanopore) from human and mouse samples.
Comprehensively evaluated circRNA assembly algorithms: circseq_cup, CIRI_full, and CircAST.
Developed and applied a screening protocol to filter assembled circRNA sequences.

Main Results:

No single short-read assembly algorithm performed best across all datasets (human vs. mouse).
CIRI-full showed superior performance in human datasets, while CircAST excelled in mouse datasets.
The developed screening protocol enhanced the performance of CIRI-full for both human and mouse datasets, improving F1 scores.

Conclusions:

Combining multiple circRNA assembly algorithms is recommended for robust sequence reconstruction.
Long-read sequencing is valuable for assessing and validating circRNA assembly accuracy.
The proposed screening protocol refines circRNA identification, particularly for exonic circRNAs.