Computational approaches for detecting disease-associated alternative splicing events.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Alternative splicing (AS) is a fundamental gene expression regulation mechanism.
• AS events are increasingly linked to the development of complex diseases.
• Computational approaches are vital for identifying disease-associated AS.

Purpose of the Study:

• To systematically review computational methods for detecting disease-associated AS events.
• To discuss metrics for quantifying AS events.
• To explore methods for identifying genetic variants regulating splicing.

Main Methods:

• Review of existing literature on computational methods for AS analysis.
• Categorization of methods into differential splicing analysis, aberrant splicing detection, and network analysis.
• Experimental comparison of method performance.

Main Results:

• Description of quantitative metrics for AS events.
• Discussion of three main computational approaches for disease-associated AS detection.
• Presentation of methods for identifying splicing-regulatory genetic variants.

Conclusions:

• The review provides a systematic overview of computational tools for analyzing disease-associated splicing.
• Understanding these methods is key to elucidating the genetic basis of complex diseases.
• Identified limitations and future directions for computational AS analysis.