DIEGO: detection of differential alternative splicing using Aitchison's geometry

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Alternative splicing is crucial for eukaryotic gene regulation, cell differentiation, and is linked to various diseases.
• RNA-Sequencing enables detailed investigation of differentially expressed isoforms.
• Existing differential alternative splicing (DAS) analysis tools often overlook split reads, which are direct evidence of splicing events.

Purpose of the Study:

• To present DIEGO, a novel compositional data analysis method for detecting DAS.
• To leverage split reads for more accurate DAS analysis.
• To provide a robust and efficient tool for analyzing large-scale RNA-Seq experiments.

Main Methods:

• Development of DIEGO, a compositional data analysis method.
• Utilizing split reads as the primary data for DAS detection.
• Implementation in Python with parsers for common formats.

Main Results:

• DIEGO accurately detects differential alternative splicing (DAS) using split reads.
• The tool operates without requiring prior isoform annotations.
• DIEGO is efficient for large datasets, demonstrating robustness and accuracy.

Conclusions:

• DIEGO offers an advancement in DAS analysis by incorporating split read information.
• The method provides a fast, accurate, and annotation-independent approach to studying alternative splicing.
• This tool facilitates deeper investigation into the role of alternative splicing in biological processes and disease.