Selecting Differential Splicing Methods: Practical Considerations

TL;DR

This review compares 22 bioinformatics tools for differential splicing analysis, highlighting their methods, applications, and challenges, and provides guidance for tool selection amid evolving sequencing technologies.

Contribution

It offers a comprehensive categorization and evaluation of existing differential splicing tools, and proposes a practical guide for selecting appropriate methods based on data and analysis level.

Findings

No consensus on tool performance across scenarios

Tools like DEXSeq and rMATS are highly cited and maintained

Advances in long-read sequencing will influence future tool development

Abstract

Alternative splicing is crucial in gene regulation, with significant implications in clinical settings and biotechnology. This review article compiles bioinformatics RNA-seq tools for investigating differential splicing; offering a detailed examination of their statistical methods, case applications, and benefits. A total of 22 tools are categorised by their statistical family (parametric, non-parametric, and probabilistic) and level of analysis (transcript, exon, and event). The central challenges in quantifying alternative splicing include correct splice site identification and accurate isoform deconvolution of transcripts. Benchmarking studies show no consensus on tool performance, revealing considerable variability across different scenarios. Tools with high citation frequency and continued developer maintenance, such as DEXSeq and rMATS, are recommended for prospective researchers. To aid in tool selection, a guide schematic is proposed based on variations in data input and the required level of analysis. Additionally, advancements in long-read RNA sequencing are expected to drive the evolution of differential splicing tools, reducing the need for isoform deconvolution and prompting further innovation.