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

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

Published: 2024-02-23 DOI: 10.1038/s41596-023-00944-2

RNA-seq

Alternative Splicing

rMATS-turbo

Large-scale Data Analysis

Computational Workflow

AI 解读

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.

Using high-resolution microscopy data to generate realistic structures for electromagnetic FDTD simulations from complex biological models

Experimental modulation of physiological force application on leg joint neurons in intactDrosophila melanogaster

Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility

Identification, isolation and analysis of human gut-associated lymphoid tissues

Development of RNA G-quadruplex (rG4)-targetingl-RNA aptamers by rG4-SELEX

Self-assembled ultraflexible probes for long-term neural recordings and neuromodulation

Hyperpolarized water as universal sensitivity booster in biomolecular NMR

Improved sampling and DNA extraction procedures for microbiome analysis in food-processing environments

An analytical workflow for dynamic characterization and quantification of metal-bearing nanomaterials in biological matrices

A flow cytometry-based protocol for syngenic isolation of neurovascular unit cells from mouse and human tissues

查看全部

Sections

Figures

References

Supplementary information

Wang, Y. et al. Role of Hakai in m(6)A modification pathway in Drosophila. Nat. Commun. 12, 2159 (2021).
Lau, E. et al. Splice-junction-based mapping of alternative isoforms in the human proteome. Cell Rep. 29, 3751–3765 e3755 (2019).
Daniels, N. J. et al. Functional analyses of human LUC7-like proteins involved in splicing regulation and myeloid neoplasms. Cell Rep. 35, 108989 (2021).
Zhang, Y. et al. Regional variation of splicing QTLs in human brain. Am. J. Hum. Genet. 107, 196–210 (2020).
Heber, S., Alekseyev, M., Sze, S. H., Tang, H. & Pevzner, P. A. Splicing graphs and EST assembly problem. Bioinformatics 18, S181–S188 (2002).
Xing, Y., Resch, A. & Lee, C. The multiassembly problem: reconstructing multiple transcript isoforms from EST fragment mixtures. Genome Res. 14, 426–441 (2004).
Rahman, M. A., Krainer, A. R. & Abdel-Wahab, O. SnapShot: splicing alterations in cancer. Cell 180, 208–208 e201 (2020).
Anczukow, O. & Krainer, A. R. Splicing-factor alterations in cancers. RNA 22, 1285–1301 (2016).
Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015).
Nilsen, T. W. & Graveley, B. R. Expansion of the eukaryotic proteome by alternative splicing. Nature 463, 457–463 (2010).
Sharp, P. A. Split genes and RNA splicing. Cell 77, 805–815 (1994).
Wang, Z. & Burge, C. B. Splicing regulation: from a parts list of regulatory elements to an integrated splicing code. RNA 14, 802–813 (2008).
Fu, X. D. & Ares, M. Jr Context-dependent control of alternative splicing by RNA-binding proteins. Nat. Rev. Genet. 15, 689–701 (2014).
Kalsotra, A. & Cooper, T. A. Functional consequences of developmentally regulated alternative splicing. Nat. Rev. Genet. 12, 715–729 (2011).
Baralle, F. E. & Giudice, J. Alternative splicing as a regulator of development and tissue identity. Nat. Rev. Mol. Cell Biol. 18, 437–451 (2017).
Scotti, M. M. & Swanson, M. S. RNA mis-splicing in disease. Nat. Rev. Genet. 17, 19–32 (2016).
Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
Park, E., Pan, Z., Zhang, Z., Lin, L. & Xing, Y. The expanding landscape of alternative splicing variation in human populations. Am. J. Hum. Genet. 102, 11–26 (2018).
Pan, Q., Shai, O., Lee, L. J., Frey, B. J. & Blencowe, B. J. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat. Genet. 40, 1413–1415 (2008).
Wang, E. T. et al. Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470–476 (2008).
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).
Alamancos, G. P., Agirre, E. & Eyras, E. Methods to study splicing from high-throughput RNA sequencing data. Methods Mol. Biol. 1126, 357–397 (2014).
Conesa, A. et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016).
Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. 20, 631–656 (2019).
Pan, Y. et al. RNA dysregulation: an expanding source of cancer immunotherapy targets. Trends Pharmacol. Sci. 42, 268–282 (2021).
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-seq data. Proc. Natl Acad. Sci. USA 111, E5593–E5601 (2014).
Katz, Y., Wang, E. T., Airoldi, E. M. & Burge, C. B. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat. Methods 7, 1009–1015 (2010).
Van Nostrand, E. L. et al. A large-scale binding and functional map of human RNA-binding proteins. Nature 583, 711–719 (2020).
Begg, B. E., Jens, M., Wang, P. Y., Minor, C. M. & Burge, C. B. Concentration-dependent splicing is enabled by Rbfox motifs of intermediate affinity. Nat. Struct. Mol. Biol. 27, 901–912 (2020).
Hu, X. et al. The RNA-binding protein AKAP8 suppresses tumor metastasis by antagonizing EMT-associated alternative splicing. Nat. Commun. 11, 486 (2020).
Jourdain, A. A. et al. Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS. Mol. Cell 81, 1905–1919 e1912 (2021).
Leclair, N. K. et al. Poison exon splicing regulates a coordinated network of SR protein expression during differentiation and tumorigenesis. Mol. Cell 80, 648–665 e649 (2020).
Liu, W. et al. Ectopic targeting of CG DNA methylation in Arabidopsis with the bacterial SssI methyltransferase. Nat. Commun. 12, 3130 (2021).
Wang, L. et al. RALF1–FERONIA complex affects splicing dynamics to modulate stress responses and growth in plants. Sci. Adv. 6, eaaz1622 (2020).
Phillips, J. W. et al. Pathway-guided analysis identifies Myc-dependent alternative pre-mRNA splicing in aggressive prostate cancers. Proc. Natl Acad. Sci. USA 117, 5269–5279 (2020).
Mironov, A., Denisov, S., Gress, A., Kalinina, O. V. & Pervouchine, D. D. An extended catalogue of tandem alternative splice sites in human tissue transcriptomes. PLoS Comput. Biol. 17, e1008329 (2021).
Demirdjian, L. et al. Detecting allele-specific alternative splicing from population-scale RNA-seq data. Am. J. Hum. Genet. 107, 461–472 (2020).
Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Anders, S., Reyes, A. & Huber, W. Detecting differential usage of exons from RNA-seq data. Genome Res. 22, 2008–2017 (2012).
Wu, J. et al. SpliceTrap: a method to quantify alternative splicing under single cellular conditions. Bioinformatics 27, 3010–3016 (2011).
Alamancos, G. P., Pages, A., Trincado, J. L., Bellora, N. & Eyras, E. Leveraging transcript quantification for fast computation of alternative splicing profiles. RNA 21, 1521–1531 (2015).
Trincado, J. L. et al. SUPPA2: fast, accurate, and uncertainty-aware differential splicing analysis across multiple conditions. Genome Biol. 19, 40 (2018).
Vaquero-Garcia, J. et al. RNA splicing analysis using heterogeneous and large RNA-seq datasets. Nat. Commun. 14, 1230 (2023).
Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).
Lin, K. T. & Krainer, A. R. PSI-Sigma: a comprehensive splicing-detection method for short-read and long-read RNA-seq analysis. Bioinformatics 35, 5048–5054 (2019).
Sterne-Weiler, T., Weatheritt, R. J., Best, A. J., Ha, K. C. H. & Blencowe, B. J. Efficient and accurate quantitative profiling of alternative splicing patterns of any complexity on a laptop. Mol. Cell 72, 187–200 e186 (2018).
Wang, Q. & Rio, D. C. JUM is a computational method for comprehensive annotation-free analysis of alternative pre-mRNA splicing patterns. Proc. Natl Acad. Sci. USA 115, E8181–E8190 (2018).
Mehmood, A. et al. Systematic evaluation of differential splicing tools for RNA-seq studies. Brief. Bioinform. 21, 2052–2065 (2020).
Muller, I. B. et al. Computational comparison of common event-based differential splicing tools: practical considerations for laboratory researchers. BMC Bioinform. 22, 347 (2021).
Vaquero-Garcia, J. et al. A new view of transcriptome complexity and regulation through the lens of local splicing variations. eLife 5, e11752 (2016).
Amarasinghe, S. L. et al. Opportunities and challenges in long-read sequencing data analysis. Genome Biol. 21, 30 (2020).
Byrne, A., Cole, C., Volden, R. & Vollmers, C. Realizing the potential of full-length transcriptome sequencing. Philos. Trans. R. Soc. Lond. B 374, 20190097 (2019).
Gao, Y. et al. ESPRESSO: robust discovery and quantification of transcript isoforms from error-prone long-read RNA-seq data. Sci. Adv. 9, eabq5072 (2023).
Zhang, Z. et al. Deep-learning augmented RNA-seq analysis of transcript splicing. Nat. Methods 16, 307–310 (2019).
Lu, Z. X. et al. Transcriptome-wide landscape of pre-mRNA alternative splicing associated with metastatic colonization. Mol. Cancer Res. 13, 305–318 (2015).
Yang, J. et al. Guidelines and definitions for research on epithelial–mesenchymal transition. Nat. Rev. Mol. Cell Biol. 21, 341–352 (2020).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
Chakraborty, P., George, J. T., Tripathi, S., Levine, H. & Jolly, M. K. Comparative study of transcriptomics-based scoring metrics for the epithelial–hybrid–mesenchymal spectrum. Front. Bioeng. Biotechnol. 8, 220 (2020).
Tan, T. Z. et al. Epithelial–mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients. EMBO Mol. Med. 6, 1279–1293 (2014).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Veeneman, B. A., Shukla, S., Dhanasekaran, S. M., Chinnaiyan, A. M. & Nesvizhskii, A. I. Two-pass alignment improves novel splice junction quantification. Bioinformatics 32, 43–49 (2016).
Wang, Y. et al. rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data. rMATS-turbo-tutorial https://doi.org/10.5281/zenodo.7931186 (2023).

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

Supplementary information

推荐阅读