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Updated: Mar 5, 2026

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Circular RNA identification based on multiple seed matching.

Yuan Gao¹, Jinyang Zhang¹, Fangqing Zhao²

¹University of Chinese Academy of Sciences.

Briefings in Bioinformatics

|March 24, 2017

Summary

This summary is machine-generated.

CIRI2 is a new computational tool that accurately detects circular RNAs (circRNAs) by improving back-spliced junction identification and filtering false positives. This advanced circRNA detection method offers balanced sensitivity, reliability, and efficiency.

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Last Updated: Mar 5, 2026

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:

Bioinformatics
Molecular Biology
Genomics

Background:

Computational tools are crucial for studying circular RNA (circRNA) biogenesis and function.
Existing circRNA detection tools have limitations in accuracy and efficiency.

Purpose of the Study:

To develop an improved computational tool for accurate circRNA detection.
To enhance the identification of back-spliced junction reads and minimize false positives.

Main Methods:

Developed CIRI2, a multithreading circRNA detection tool utilizing adapted maximum likelihood estimation and multiple seed matching.
Established objective assessment criteria using real data from RNase R-treated samples.
Systematically compared CIRI2 against 10 other widely used circRNA detection tools.

Main Results:

CIRI2 demonstrated superior performance compared to its predecessor CIRI and other existing tools.
The tool exhibited a balanced performance in sensitivity, reliability, processing duration, and RAM usage.
CIRI2 effectively filtered false positives arising from repetitive sequences and mapping errors.

Conclusions:

CIRI2 represents a significant advancement in circRNA detection methodology.
The tool offers a more accurate, reliable, and efficient solution for circRNA identification in bioinformatics studies.