AQUARIUM: accurate quantification of circular isoforms using model-based strategy.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Current circular RNA (circRNA) quantification methods often rely on back-splicing junctions (BSJ), which can lead to biased expression estimates due to uneven sequencing read distribution.
• Previous model-based strategies improved circRNA quantification by considering entire transcripts, but accuracy was limited by the lack of precise circRNA structural information.

Purpose of the Study:

• To develop a substantially improved circRNA quantification tool, AQUARIUM, that incorporates the full-length RNA structure of circular isoforms.
• To enhance the accuracy and reliability of circRNA expression estimation.

Main Methods:

• Developed AQUARIUM, a novel computational tool for circRNA quantification.
• Introduced the full-length RNA structure of circular isoforms into the quantification model.
• Evaluated AQUARIUM's performance using both biological and simulated rRNA-depleted RNA-seq datasets.

Main Results:

• Demonstrated superior performance of AQUARIUM in circRNA quantification compared to existing methods.
• Showcased improved accuracy at both the back-splicing junction (BSJ) and isoform levels.
• Validated performance on diverse RNA-seq datasets.

Conclusions:

• AQUARIUM offers a significant advancement in circRNA quantification by leveraging complete circular isoform structures.
• The tool provides more accurate and reliable circRNA expression estimates, crucial for downstream biological analyses.
• AQUARIUM represents a valuable resource for researchers studying circRNA biology.