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Computational approaches for detecting disease-associated alternative splicing events.

Jiashu Liu¹, Cui-Xiang Lin¹, Xiaoqi Zhang¹

¹School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China.

Briefings in Bioinformatics

|March 29, 2023

Summary

Alternative splicing (AS) is crucial in complex diseases. This review details computational methods to detect disease-associated AS events and their genetic regulators, aiding disease mechanism understanding.

Keywords:

aberrant splicing alternative splicing differential splicing disease splicing QTL splicing-related network

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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.