IsopretGO-analysing and visualizing the functional consequences of differential splicing.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene Ontology overrepresentation analysis (GO-ORA) is standard for characterizing differentially expressed genes (DGE) in RNA sequencing (RNA-seq).
• GO-ORA is not applicable for characterizing differential alternative splicing (DAS).
• Functional implications of DAS remain underexplored.

Purpose of the Study:

• Introduce isopretGO, a desktop application for visualizing functional implications of both DGE and DAS.
• Leverage machine learning predictions of Gene Ontology (GO) annotations for individual isoforms.
• Enable the identification of functional shifts in RNA-seq data attributed to differential splicing.

Main Methods:

• Developed isopretGO, a desktop application.
• Integrated machine learning-based GO annotation predictions for isoforms.
• Analyzed 100 RNA-seq datasets to compare functional profiles of DGE and DAS.

Main Results:

• DAS and DGE frequently exhibit significantly different functional profiles.
• Demonstrated the utility of isopretGO in identifying functional shifts.
• Highlighted the importance of considering DAS for a comprehensive understanding of RNA-seq data.

Conclusions:

• isopretGO provides a novel approach to functionally characterize DAS.
• Functional analysis of RNA-seq data should consider both DGE and DAS.
• isopretGO facilitates the discovery of splicing-specific functional insights.