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  • Review Article
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Transcriptomics in the era of long-read sequencing

Nature Reviews Genetics volume 26, pages 681–701 (2025) Cite this article

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Abstract

Transcriptome sequencing revolutionized the analysis of gene expression, providing an unbiased approach to gene detection and quantification that enabled the discovery of novel isoforms, alternative splicing events and fusion transcripts. However, although short-read sequencing technologies have surpassed the limited dynamic range of previous technologies such as microarrays, they have limitations, for example, in resolving full-length transcripts and complex isoforms. Over the past 5 years, long-read sequencing technologies have matured considerably, with improvements in instrumentation and analytical methods, enabling their application to RNA sequencing (RNA-seq). Benchmarking studies are beginning to identify the strengths and limitations of long-read RNA-seq, although there remains a need for comprehensive resources to guide newcomers through the intricacies of this approach. In this Review, we provide a comprehensive overview of the long-read RNA-seq workflow, from library preparation and sequencing challenges to core data processing, downstream analyses and emerging developments. We present an extensive inventory of experimental and analytical methods and discuss current challenges and prospects.

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Fig. 1: Overview of long-read transcriptomics, applications and computational analyses.
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Fig. 2: Commercial and non-commercial library preparation methods.
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Fig. 3: Approaches for transcript identification and quantification using long-read RNA sequencing data.
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Fig. 4: Downstream analysis and emerging applications for long-read RNA-seq studies.
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Acknowledgements

The authors’ work has been funded by the European Union Marie Skłodowska-Curie Actions Postdoctoral Fellowship (HORIZON-MSCA-2023-PF-01-01 grant agreement project 101149931), the European Union Marie Skłodowska-Curie Actions Doctoral Network project LongTREC (HORIZON-MSCA-2021-DN-01 grant agreement project 101072892) and the Spanish Science Ministry, grant number PID2020-119537RB-I00.

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  1. These authors contributed equally: Carolina Monzó, Tianyuan Liu.

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  1. Institute for Integrative Systems Biology, Spanish National Research Council, Paterna, Valencia, Spain

    Carolina Monzó, Tianyuan Liu & Ana Conesa

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  1. Carolina Monzó
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Correspondence to Carolina Monzó or Ana Conesa.

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A.C. has received in-kind funding from Pacific Biosciences for library preparation and sequencing. A.C. and T.L. collaborate with Oxford Nanopore in the Marie Skłodowska-Curie Actions Doctoral Network project LongTREC. C.M. declares no competing interests.

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CycloneSEQ: https://en.mgitech.cn/

Dorado: https://github.com/nanoporetech/dorado

LyRic: https://github.com/guigolab/LyRic

nf-core/nanoseq: https://nf-co.re/nanoseq/3.1.0/

NVIDIA: https://nvidia.com/en-us/case-studies/long-read-sequencing/

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Monzó, C., Liu, T. & Conesa, A. Transcriptomics in the era of long-read sequencing. Nat Rev Genet 26, 681–701 (2025). https://doi.org/10.1038/s41576-025-00828-z

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