# Selecting differential splicing methods: Practical considerations

## TL;DR

This review evaluates 22 bioinformatics tools for analyzing differential splicing, highlighting their strengths and challenges in RNA-seq data analysis.

## Contribution

The paper provides a practical guide and schematic for selecting differential splicing tools based on data input and analysis level.

## Key findings

- Benchmarking studies show no consensus on the best tool for differential splicing analysis.
- DEXSeq and rMATS are recommended due to high citation frequency and active maintenance.
- Long-read RNA sequencing may reduce the need for isoform deconvolution in future tools.

## 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.