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Updated: Mar 1, 2026
08:25
Identification of Circular RNAs using RNA Sequencing
Published on: November 14, 2019
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A comprehensive overview and evaluation of circular RNA detection tools
Xiangxiang Zeng1, Wei Lin1, Maozu Guo2
1School of Information Science and Engineering, Xiamen University, Xiamen, China.
Plos Computational Biology
|June 9, 2017
Summary
This study introduces a new circular RNA (circRNA) simulator and compares 11 detection tools. CIRI, CIRCexplorer, and KNIFE showed balanced performance in identifying circRNAs.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Circular RNAs (circRNAs) are formed by backsplicing and have roles as miRNA sponges, transcriptional regulators, and biomarkers.
- Genome-wide identification of circRNAs is enabled by large-scale transcriptome data.
- Numerous circRNA detection tools exist, necessitating a performance comparison and user guidelines.
Purpose of the Study:
- To develop an improved, user-friendly circRNA read simulator supporting CircBase.
- To comprehensively and unbiasedly compare the performance of 11 circRNA detection tools.
- To provide guidelines for selecting appropriate circRNA detection pipelines.
Main Methods:
- Developed a circRNA read simulator mimicking backsplicing events.
- Evaluated 11 circRNA detection tools using simulated and real transcriptome datasets.
- Assessed tool performance using precision, sensitivity, F1 score, and Area Under Curve metrics.
Main Results:
- No single circRNA detection tool excelled across all performance metrics.
- The developed simulator effectively generates realistic backsplicing reads.
- CIRI, CIRCexplorer, and KNIFE demonstrated superior, balanced performance in precision and sensitivity.
Conclusions:
- A comprehensive comparison of circRNA detection tools is crucial for accurate identification.
- The developed simulator aids in benchmarking circRNA detection methods.
- CIRI, CIRCexplorer, and KNIFE are recommended for balanced circRNA detection performance.
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