We conducted a comprehensive long-read RNA sequencing (RNA-seq) benchmarking experiment by combining spike-ins and in silico mixtures to establish a ground-truth dataset. We used long- and short-read RNA-seq technology to deeply sequence samples and compared the performance of a range of analysis tools on these data.
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References
Method of the Year 2022: long-read sequencing. Nat. Methods 20, 1 (2023). An editorial that introduces long-read sequencing technologies as Nature Methods’ Method of the Year 2022.
Foord, C. et al. The variables on RNA molecules: concert or cacophony? Answers in long-read sequencing. Nat. Methods 20, 20–24 (2023). A review that presents the strengths of long-read technologies in solving the complexity of the transcriptome.
Hardwick, S. A. et al. Spliced synthetic genes as internal controls in RNA sequencing experiments. Nat. Methods 13, 792–798 (2016). This paper introduces the sequin spike-in standards.
Holik, A. Z. et al. RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods. Nucleic Acids Res. 45, e30 (2017). This paper reports a short-read RNA-seq benchmarking study using a mixture design.
Tian, L. et al. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat. Methods 16, 479–487 (2019). This paper reports a single-cell RNA-seq benchmarking study that uses a mixture design.
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This is a summary of: Dong, X. et al. Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures. Nat. Methods https://doi.org/10.1038/s41592-023-02026-3 (2023).
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Evaluating long-read RNA-sequencing analysis tools with in silico mixtures. Nat Methods 20, 1643–1644 (2023). https://doi.org/10.1038/s41592-023-02027-2
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DOI: https://doi.org/10.1038/s41592-023-02027-2
