A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level

Abstract RNA alternative splicing, a post-transcriptional stage in eukaryotes, is crucial in cellular homeostasis and disease processes. Due to the rapid development of the next-generation sequencing (NGS) technology and the flood of NGS data, the detection of differential splicing from RNA-seq data...

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Veröffentlicht in:Briefings in bioinformatics 2023-05, Vol.24 (3)
Hauptverfasser: Jiang, Minghao, Zhang, Shiyan, Yin, Hongxin, Zhuo, Zhiyi, Meng, Guoyu
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Sprache:eng
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Zusammenfassung:Abstract RNA alternative splicing, a post-transcriptional stage in eukaryotes, is crucial in cellular homeostasis and disease processes. Due to the rapid development of the next-generation sequencing (NGS) technology and the flood of NGS data, the detection of differential splicing from RNA-seq data has become mainstream. A range of bioinformatic tools has been developed. However, until now, an independent and comprehensive comparison of available algorithms/tools at the event level is still lacking. Here, 21 different tools are subjected to systematic evaluation, based on simulated RNA-seq data where exact differential splicing events are introduced. We observe immense discrepancies among these tools. SUPPA, DARTS, rMATS and LeafCutter outperforme other event-based tools. We also examine the abilities of the tools to identify novel splicing events, which shows that most event-based tools are unsuitable for discovering novel splice sites. To improve the overall performance, we present two methodological approaches i.e. low-expression transcript filtering and tool-pair combination. Finally, a new protocol of selecting tools to perform differential splicing analysis for different analytical tasks (e.g. precision and recall rate) is proposed. Under this protocol, we analyze the distinct splicing landscape in the DUX4/IGH subgroup of B-cell acute lymphoblastic leukemia and uncover the differential splicing of TCF12. All codes needed to reproduce the results are available at https://github.com/mhjiang97/Benchmarking_DS.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbad121