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Review
. 2025 May 30:14:47.
doi: 10.12688/f1000research.155223.2. eCollection 2025.

Selecting differential splicing methods: Practical considerations for short-read RNA sequencing

Ben J Draper  1 , Mark J Dunning  2 , David C James  1
Affiliations
Review

Selecting differential splicing methods: Practical considerations for short-read RNA sequencing

Ben J Draper et al. F1000Res. .

Abstract

Alternative splicing is crucial in gene regulation, with significant implications in clinical settings and biotechnology. This review article compiles bioinformatics short-read 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. Emerging long-read RNA sequencing technologies are discussed as a complement to short-read methods, promising reduced deconvolution needs and further innovation.

Keywords: Alternative Splicing; Bioinformatics; Differential Expression; RNASeq; Transcriptomics.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Timeline of statistical methods in differential splicing tool development.
Methods are categorized into parametric and non-parametric approaches, grouped by methodological families. The classification is based on the underlying statistical procedures used for modelling or hypothesis testing, as detailed in Supplementary Table 1. Note that some methods incorporate elements of both parametric and non-parametric frameworks, resulting in overlapping features.
Figure 2.
Figure 2.. Citation counts of differential splicing tools (2010–2024) from Web of Science (WoS) Data.
Total citation counts for surveyed differential splicing tools (2010–2024) from the Web of Science Data Portal (WoS). Tools are categorized by analysis level: event, exon, or transcript. DRIMSeq’s original paper was excluded from the citation frequency analysis as it was not indexed in WoS. Certain data included herein are derived from Clarivate Web of Science. © Copyright Clarivate 2023. All rights reserved.Total citation counts for surveyed differential splicing tools (2010–2024) from the Web of Science Data Portal (WoS). Tools are categorized by analysis level: event, exon, or transcript. DRIMSeq’s original paper was excluded from the citation frequency analysis as it was not indexed in WoS. Certain data included herein are derived from Clarivate Web of Science. © Copyright Clarivate 2023. All rights reserved.
Figure 3.
Figure 3.. Citation trends of differential splicing tools (2010–2024) from Web of Science (WoS) Data.
Annual citation frequency for current differential splicing tools (2010–2024) from Web of Science (WoS). Tools are categorized by analysis level: event, exon, or transcript. DRIMSeq’s original paper is excluded as it is not indexed in WoS. Certain data included herein are derived from Clarivate Web of Science. © Copyright Clarivate 2023. All rights reserved.
Figure 4.
Figure 4.. Developer maintenance of differential splicing tools.
Annual GitHub repository commits (2010–2024) by category, highlighting community-led maintenance of differential splicing tools. Tools without GitHub pages (MAJIQ, MISO, DSGseq, and dSpliceType) were excluded from the analysis.
Figure 5.
Figure 5.. Guideline for differential splicing tool selection based on experimental parameters.
Decision tree for differential splicing analysis, categorized by three branches based on the level of analysis. Transcript-based methods are represented in blue, exon-based methods in pink, and event-based methods in yellow.
See this image and copyright information in PMC

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