Incorporating exon-exon junction reads enhances differential splicing detection

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• RNA sequencing (RNA-seq) is crucial for gene expression studies.
• Alternative splicing generates diverse transcripts from single genes.
• Current differential exon usage (DEU) analysis may lack statistical power due to ignoring exon-junction data.

Purpose of the Study:

• To develop a novel workflow for differential splicing analysis.
• To improve the detection of differential splicing events in RNA-seq data.
• To enhance statistical power and computational efficiency in splicing analysis.

Main Methods:

• A new workflow, differential exon-junction usage (DEJU), was developed.
• DEJU integrates both exon and exon-exon junction reads for feature quantification.
• The workflow utilizes established Rsubread-edgeR/limma frameworks for statistical analysis.

Main Results:

• Comprehensive simulations demonstrated DEJU's superior performance against existing methods.
• Application to a mouse mammary gland dataset identified previously undetectable splicing events.
• Incorporating exon-exon junction reads significantly improved differential splicing detection.

Conclusions:

• The DEJU workflow significantly enhances the detection of differential splicing events.
• DEJU offers increased statistical power and computational efficiency.
• The method effectively controls the false discovery rate in splicing analysis.