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rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data

Nature Protocols volume 19, pages 1083–1104 (2024)Cite this article

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Abstract

Pre-mRNA alternative splicing is a prevalent mechanism for diversifying eukaryotic transcriptomes and proteomes. Regulated alternative splicing plays a role in many biological processes, and dysregulated alternative splicing is a feature of many human diseases. Short-read RNA sequencing (RNA-seq) is now the standard approach for transcriptome-wide analysis of alternative splicing. Since 2011, our laboratory has developed and maintained Replicate Multivariate Analysis of Transcript Splicing (rMATS), a computational tool for discovering and quantifying alternative splicing events from RNA-seq data. Here we provide a protocol for the contemporary version of rMATS, rMATS-turbo, a fast and scalable re-implementation that maintains the statistical framework and user interface of the original rMATS software, while incorporating a revamped computational workflow with a substantial improvement in speed and data storage efficiency. The rMATS-turbo software scales up to massive RNA-seq datasets with tens of thousands of samples. To illustrate the utility of rMATS-turbo, we describe two representative application scenarios. First, we describe a broadly applicable two-group comparison to identify differential alternative splicing events between two sample groups, including both annotated and novel alternative splicing events. Second, we describe a quantitative analysis of alternative splicing in a large-scale RNA-seq dataset (~1,000 samples), including the discovery of alternative splicing events associated with distinct cell states. We detail the workflow and features of rMATS-turbo that enable efficient parallel processing and analysis of large-scale RNA-seq datasets on a compute cluster. We anticipate that this protocol will help the broad user base of rMATS-turbo make the best use of this software for studying alternative splicing in diverse biological systems.

Key points

  • This protocol provides detailed guidelines for using rMATS-turbo, the latest implementation of the popular software for the discovery and quantification of alternative splicing events from RNA sequencing data. The software is exemplified in two representative scenarios.

  • rMATS-turbo incorporates a revamped computational workflow with a substantial improvement in speed and data storage efficiency. The software scales up to massive RNA sequencing datasets with tens of thousands of samples.

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Fig. 1: An overview of the rMATS-turbo workflow to discover and quantify alternative splicing events in large-scale RNA-seq datasets.
Fig. 2: General two-group comparison to identify differential alternative splicing events between PC3E and GS689 cell lines using rMATS-turbo.
Fig. 3: Alternative splicing analysis in a large-scale RNA-seq dataset (CCLE) using rMATS-turbo.

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Data availability

RNA-seq data for PC3E and GS689 cell lines (Procedure 1) and RNA-seq data for 1,019 CCLE human cancer cell lines (Procedure 2) can be downloaded from the SRA archive (https://www.ncbi.nlm.nih.gov/sra) under accessions BioProject PRJNA438990 and PRJNA523380, respectively. rMATS-turbo output files for both Procedure 1 and Procedure 2 datasets are available at https://doi.org/10.5281/zenodo.6647023 and other result files are provided in the companion GitHub repository of this protocol (https://github.com/Xinglab/rmats-turbo-tutorial63).

Code availability

rMATS-turbo is publicly available on GitHub (https://github.com/Xinglab/rmats-turbo) and Bioconda (https://anaconda.org/bioconda/rmats). rmats2sashimiplot is publicly available on GitHub (https://github.com/Xinglab/rmats2sashimiplot). Custom scripts for data analysis and visualization of the results generated in Procedures 1 and 2 are provided in the companion GitHub repository of this protocol (https://github.com/Xinglab/rmats-turbo-tutorial63).

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Acknowledgements

This work was supported by National Institutes of Health grants R01GM088342 and R01GM117624.

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Author notes

  1. These authors contributed equally: Yuanyuan Wang, Zhijie Xie.

Authors and Affiliations

  1. Bioinformatics Interdepartmental Graduate Program, University of California, Los Angeles, Los Angeles, CA, USA

    Yuanyuan Wang

  2. Center for Computational and Genomic Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Yuanyuan Wang, Zhijie Xie, Eric Kutschera, Jenea I. Adams, Kathryn E. Kadash-Edmondson & Yi Xing

  3. Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA

    Jenea I. Adams

  4. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yi Xing

  5. Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Yi Xing

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Contributions

Y.X. conceived the study. Y.W., Z.X. and Y.X. designed the research and developed the methodology. Y.W., Z.X. and E.K. contributed to analytic tools. Y.W., E.K., J.I.A. and Y.X. analyzed the data. Y.W., K.E.K.-E. and Y.X. wrote the paper with input from all other authors.

Corresponding author

Correspondence to Yi Xing.

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Competing interests

Y.X. is a scientific cofounder of Panorama Medicine. Y.X. and Z.X. receive licensing income for commercial usage of rMATS-turbo. The remaining authors declare no competing interests.

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Nature Protocols thanks Yiwen Chen and Julien Gagneur for their contribution to the peer review of this work.

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Key references using this protocol

Phillips, J. W. et al. Proc. Natl Acad. Sci. USA 117, 5269–5279 (2020): https://doi.org/10.1073/pnas.1915975117

Shen, S. et al. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014): https://doi.org/10.1073/pnas.1419161111

Key data used in this protocol

Zhang, Z. et al. Nat. Methods 16, 307–310 (2019): https://doi.org/10.1038/s41592-019-0351-9

Supplementary information

Supplementary Information

Supplementary Figs. 1–3.

Reporting Summary

Supplementary Tables 1, 2

SRA accession numbers and sample information for examples 1 and 2.

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Wang, Y., Xie, Z., Kutschera, E. et al. rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data. Nat Protoc 19, 1083–1104 (2024). https://doi.org/10.1038/s41596-023-00944-2

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