A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines

A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines

Nature Methods

10.1038/s41592-025-02623-4

https://doi.org/10.1038/s41592-025-02623-4

Ying Chen, Nadia M. Davidson, Yuk Kei Wan, Fei Yao, Yan Su, Hasindu Gamaarachchi, Andre Sim, Harshil Patel, Hwee Meng Low, Christopher Hendra, Laura Wratten, Christopher Hakkaart, Chelsea Sawyer, Viktoriia Iakovleva, Puay Leng Lee, Lixia Xin, Hui En Vanessa Ng, Jia Min Loo, Xuewen Ong, Hui Qi Amanda Ng, Jiaxu Wang, Wei Qian Casslynn Koh, Suk Yeah Polly Poon, Dominik Stanojevic, Hoang-Dai Tran, Kok Hao Edwin Lim, Shen Yon Toh, Philip Andrew Ewels, Huck-Hui Ng, N. Gopalakrishna Iyer, Alexandre Thiery, Wee Joo Chng, Leilei Chen, Ramanuj DasGupta, Mile Sikic, Yun-Shen Chan, Boon Ooi Patrick Tan, Yue Wan, Wai Leong Tam, Qiang Yu, Chiea Chuan Khor, Torsten Wüstefeld, Alexander Lezhava, Ploy N. Pratanwanich, Michael I. Love, Wee Siong Sho Goh, Sarah B. Ng, Alicia Oshlack, , N. Gopalakrishna Iyer, Qiang Yu, Jonathan Göke

13 March 2025

Chen, Y., Davidson, N. M., Wan, Y. K., Yao, F., Su, Y., Gamaarachchi, H., Sim, A., Patel, H., Low, H. M., Hendra, C., Wratten, L., Hakkaart, C., Sawyer, C., Iakovleva, V., Lee, P. L., Xin, L., Ng, H. E. V., Loo, J. M., Ong, X., … Göke, J. (2025). A systematic benchmark of Nanopore long-read RNA sequencing for transcript-level analysis in human cell lines. Nature Methods, 22(4), 801–812. https://doi.org/10.1038/s41592-025-02623-4

The human genome contains instructions to transcribe more than 200,000 RNAs. However, many RNA transcripts are generated from the same gene, resulting in alternative isoforms that are highly similar and that remain difficult to quantify. To evaluate the ability to study RNA transcript expression, we profiled seven human cell lines with five different RNA-sequencing protocols, including short-read cDNA, Nanopore long-read direct RNA, amplification-free direct cDNA and PCR-amplified cDNA sequencing, and PacBio IsoSeq, with multiple spike-in controls, and additional transcriptome-wide N6-methyladenosine profiling data. We describe differences in read length, coverage, throughput and transcript expression, reporting that long-read RNA sequencing more robustly identifies major isoforms. We illustrate the value of the SG-NEx data to identify alternative isoforms, novel transcripts, fusion transcripts and N6-methyladenosine RNA modifications. Together, the SG-NEx data provide a comprehensive resource enabling the development and benchmarking of computational methods for profiling complex transcriptional events at isoform-level resolution.

Attribution 4.0 International (CC BY 4.0)

This research / project is supported by the National Medical Research Council - Open Fund - Individual Research Grant
Grant Reference no. : OFIRG16nov019

This research / project is supported by the National Medical Research Council - Open Fund - Individual Research Grant
Grant Reference no. : OFIRG20nov-0108

This research / project is supported by the Ministry of Education - Academic Research Fund Tier 3
Grant Reference no. : MOE-MOET32023-0002

This research / project is supported by the National Research Foundation - Competitive Research Foundation
Grant Reference no. : NRF-CRP26-2021-0001

This research / project is supported by the National Research Foundation - NRF Investigatorship
Grant Reference no. : NRF-NRFI08-2022-0007

https://oar.a-star.edu.sg/communities-collections/articles/20818

1548-7091
1548-7105

Genome Institute of Singapore

https://doi.org/10.1038/s41592-025-02623-4