COL: a pipeline for identifying putatively functional back-splicing

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Circular RNAs (circRNAs) are non-coding RNAs formed by back-splicing.
• While most circRNAs are non-functional, a subset possesses biological roles.
• Distinguishing functional circRNAs from non-functional ones is crucial but difficult due to limitations in current experimental and computational methods.

Purpose of the Study:

• To develop a novel computational method for identifying putatively functional circRNAs.
• To address the limitations of low-throughput experimental techniques and unreliable computational approaches.

Main Methods:

• The study hypothesized that functional back-splicing events exhibit higher rates than expected, possess conserved splicing motifs, and show unusually high back-splicing levels.
• These features were confirmed in conserved back-splicing events across human, macaque, and mouse.
• A computational pipeline named COL was designed, integrating these three features to predict functional back-splicing.

Main Results:

• COL can predict functional back-splicing using a single biological sample, unlike methods requiring multiple samples.
• COL demonstrates a lower false positive rate compared to methods relying solely on back-splicing levels.
• The pipeline successfully identified putatively functional back-splicing events.

Conclusions:

• COL is an efficient and versatile computational tool for the rapid identification of potentially functional circRNAs.
• The identified circRNAs are suitable for experimental validation.
• COL is publicly available, facilitating further research in the field.