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Systematic evaluation of computational tools for multitype RNA modification detection using nanopore direct RNA sequencing

Nature Methods volume 23, pages 438–450 (2026)Cite this article

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Abstract

Nanopore direct RNA sequencing offers a versatile approach for detecting multiple types of RNA modifications at a single-base resolution. In this study, we systematically evaluate 86 computational tools for detecting six RNA modifications (m6A, Ψ, m5C, A-to-I editing, m7G and m1A) using direct RNA sequencing data from both RNA002 and RNA004 chemistries. We demonstrate that retraining tools with a combination of in vitro transcription and real biological samples notably enhances both accuracy and generalizability over their original implementations, especially for Ψ, m5C and A-to-I. Evaluations on real biological samples reveal that while m6A detection tools generally achieve high accuracy, non-m6A tools struggle with precision–recall balance, quantification accuracy and biological validity. Our findings highlight the importance of incorporating diverse training data and stress the need for tools capable of reliably distinguishing between modification types at single-base resolution. These insights provide a foundation for advancing RNA modification detection.

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Fig. 1: Overview of the evaluated tools and study design.
Fig. 2: Retraining improves prediction performance and generalizability.
Fig. 3: Accuracy evaluation.
Fig. 4: Biological validity evaluation.
Fig. 5: Robustness evaluation.
Fig. 6: Summary of performance.

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Data availability

Published RNA002 datasets from WT and METTL3 KO HEK293T cells were obtained from the ENA under accession number PRJEB40872 (ref. 24). RNA002 data from H9 cells were obtained from the National Center for Biotechnology Information (NCBI) SRA under accession number SRP363295 (ref. 26). RNA002 data from HeLa cells and corresponding IVT samples were downloaded from ENA under accession number PRJNA777450 (ref. 28). RNA002 data of A549 used in this study were downloaded from NCBI SRA database under accession code PRJNA1108269 (ref. 57). RNA002 data of fully modified IVT for m1A, m5C and m6A, as well as fully unmodified IVT, were obtained from GEO under accession number GSE227087 (ref. 11). RNA002 data of fully modified IVT for Ψ, m5C and m7G were obtained from SRA under accession number SRP166020 (ref. 10). RNA004 data from HeLa cells, HEK293T cells and corresponding IVT samples were downloaded from ENA under accession number PRJEB80229 (ref. 53).

Code availability

The detailed command and code used for retraining and testing, along with the retrained tools, are available at https://github.com/JiejunShi/NaRMBench.

References

  1. Zhao, B. S., Roundtree, I. A. & He, C. Post-transcriptional gene regulation by mRNA modifications. Nat. Rev. Mol. Cell Biol. 18, 31–42 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Frye, M., Harada, B. T., Behm, M. & He, C. RNA modifications modulate gene expression during development. Science 361, 1346–1349 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Anderson, B. R. et al. Incorporation of pseudouridine into mRNA enhances translation by diminishing PKR activation. Nucleic Acids Res. 38, 5884–5892 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Karikó, K. et al. Incorporation of pseudouridine into mRNA yields superior nonimmunogenic vector with increased translational capacity and biological stability. Mol. Ther. 16, 1833–1840 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Liu, W.-W. et al. RNA modifications in cellular metabolism: Implications for metabolism-targeted therapy and immunotherapy. Sig. Transduct. Target Ther. 9, 1–30 (2024).

    Article  Google Scholar 

  6. Kong, Y., Mead, E. A. & Fang, G. Navigating the pitfalls of mapping DNA and RNA modifications. Nat. Rev. Genet. 24, 363–381 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Diensthuber, G. & Novoa, E. M. Charting the epitranscriptomic landscape across RNA biotypes using native RNA nanopore sequencing. Mol. Cell 85, 276–289 (2025).

    Article  CAS  PubMed  Google Scholar 

  8. Wang, Y., Zhao, Y., Bollas, A., Wang, Y. & Au, K. F. Nanopore sequencing technology, bioinformatics and applications. Nat. Biotechnol. 39, 1348–1365 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hewel, C. et al. Direct RNA sequencing enables improved transcriptome assessment and tracking of RNA modifications for medical applications. Preprint at bioRxiv https://doi.org/10.1101/2024.07.25.605188 (2025).

  10. Jenjaroenpun, P. et al. Decoding the epitranscriptional landscape from native RNA sequences. Nucleic Acids Res. 49, e7 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wu, Y. et al. Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing. Nat. Commun. 15, 4049 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Zhang, Z. et al. Systematic calibration of epitranscriptomic maps using a synthetic modification-free RNA library. Nat. Methods 18, 1213–1222 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Zhong, Z.-D. et al. Systematic comparison of tools used for m6A mapping from nanopore direct RNA sequencing. Nat. Commun. 14, 1906 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Maestri, S. et al. Benchmarking of computational methods for m6A profiling with nanopore direct RNA sequencing. Brief. Bioinform. 25, bbae001 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Esfahani, N. G., Stein, A. J., Akeson, S., Tzadikario, T. & Jain, M. Evaluation of nanopore direct RNA sequencing updates for modification detection. Preprint at bioRxiv https://doi.org/10.1101/2025.05.01.651717 (2025).

  16. Stoiber, M. et al. De novo identification of DNA modifications enabled by genome-guided nanopore signal processing. 094672 Preprint at bioRxiv https://doi.org/10.1101/094672 (2017).

  17. Liu, H. et al. Accurate detection of m6A RNA modifications in native RNA sequences. Nat. Commun. 10, 4079 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lorenz, D. A., Sathe, S., Einstein, J. M. & Yeo, G. W. Direct RNA sequencing enables m6A detection in endogenous transcript isoforms at base-specific resolution. RNA 26, 19–28 (2020).

    Article  CAS  PubMed  Google Scholar 

  19. Parker, M. T. et al. Nanopore direct RNA sequencing maps the complexity of arabidopsis mRNA processing and m6A modification. eLife 9, e49658 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Price, A. M. et al. Direct RNA sequencing reveals m6A modifications on adenovirus RNA are necessary for efficient splicing. Nat. Commun. 11, 6016 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Begik, O. et al. Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing. Nat. Biotechnol. 39, 1278–1291 (2021).

    Article  CAS  PubMed  Google Scholar 

  22. Gao, Y. et al. Quantitative profiling of N6-methyladenosine at single-base resolution in stem-differentiating xylem of populus trichocarpa using nanopore direct RNA sequencing. Genome Biol. 22, 22 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Leger, A. et al. RNA modifications detection by comparative nanopore direct RNA sequencing. Nat. Commun. 12, 7198 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pratanwanich, P. N. et al. Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat. Biotechnol. 39, 1394–1402 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Hendra, C. et al. Detection of m6A from direct RNA sequencing using a multiple instance learning framework. Nat. Methods 19, 1590–1598 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Nguyen, T. A. et al. Direct identification of a-to-I editing sites with nanopore native RNA sequencing. Nat. Methods 19, 833–844 (2022).

    Article  CAS  PubMed  Google Scholar 

  27. Qin, H. et al. DENA: Training an authentic neural network model using nanopore sequencing data of arabidopsis transcripts for detection and quantification of N6-methyladenosine on RNA. Genome Biol. 23, 25 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tavakoli, S. et al. Semi-quantitative detection of pseudouridine modifications and type I/II hypermodifications in human mRNAs using direct long-read sequencing. Nat. Commun. 14, 334 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Xie, Y.-Y. et al. Single-molecule direct RNA sequencing reveals the shaping of epitranscriptome across multiple species. Nat. Commun. 16, 5119 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Acera Mateos, P. et al. Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications. Nat. Commun. 15, 3899 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Huang, S., Wylder, A. C. & Pan, T. Simultaneous nanopore profiling of mRNA m6A and pseudouridine reveals translation coordination. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02135-0 (2024).

  32. Zhang, Y. et al. NanoMUD: profiling of pseudouridine and N1-methylpseudouridine using Oxford Nanopore direct RNA sequencing. Int. J. Biol. Macromol. 270, 132433 (2024).

    Article  CAS  PubMed  Google Scholar 

  33. Guo, W. et al. Single-molecule m6A detection empowered by endogenous labeling unveils complexities across RNA isoforms. Mol. Cell https://doi.org/10.1016/j.molcel.2025.01.014 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Liu, C. et al. Decoding the m6A epitranscriptomic landscape for biotechnological applications using a direct RNA sequencing approach. Nat. Commun. 16, 798 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Teng, H., Stoiber, M., Bar-Joseph, Z. & Kingsford, C. Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework. Genome Res. 34, 1987–1999 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Oxford Nanopore Technologies. Oxford Nanopore’s Basecaller Dorado. (2025).

  37. Xu, M. et al. BASAL: a universal mapping algorithm for nucleotide base-conversion sequencing. Nucleic Acids Res. https://doi.org/10.1093/nar/gkae1201 (2024).

  38. Lu, L. et al. Base-resolution m5C profiling across the mammalian transcriptome by bisulfite-free enzyme-assisted chemical labeling approach. Mol. Cell 84, 2984–3000.e8 (2024).

    Article  CAS  PubMed  Google Scholar 

  39. Zhang, L.-S. et al. m7 G-quant-seq: quantitative detection of RNA internal N7-methylguanosine. ACS Chem. Biol. 17, 3306–3312 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Sun, H., Li, K., Liu, C. & Yi, C. Regulation and functions of non-m6A mRNA modifications. Nat. Rev. Mol. Cell Biol. 24, 714–731 (2023).

    Article  CAS  PubMed  Google Scholar 

  41. Zhao, Z. et al. QKI shuttles internal m7G-modified transcripts into stress granules and modulates mRNA metabolism. Cell 186, 3208–3226.e27 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lo Giudice, C., Tangaro, M. A., Pesole, G. & Picardi, E. Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal. Nat. Protoc. 15, 1098–1131 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Zhang, M. et al. Quantitative profiling of pseudouridylation landscape in the human transcriptome. Nat. Chem. Biol. https://doi.org/10.1038/s41589-023-01304-7 (2023).

  44. Wei, Q. et al. Transcriptome-wide profiling of a-to-I RNA editing by SLIC-seq. Nucleic Acids Res. 51, e87 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Dai, Q. et al. Quantitative sequencing using BID-seq uncovers abundant pseudouridines in mammalian mRNA at base resolution. Nat. Biotechnol. https://doi.org/10.1038/s41587-022-01505-w (2022).

  46. Li, Y. et al. 2′-O-methylation at internal sites on mRNA promotes mRNA stability. Mol. Cell 84, 2320–2336.e6 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Hassan, D., Ariyur, A., Daulatabad, S. V., Mir, Q. & Janga, S. C. Nm-nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (nm) sites in nanopore direct RNA sequencing datasets. RNA Biol. 21, 1–15 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Jiang, S. et al. Generic diagramming platform (GDP): a comprehensive database of high-quality biomedical graphics. Nucleic Acids Res. 53, D1670–D1676 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Cui, X. et al. Guitar: an R/bioconductor package for gene annotation guided transcriptomic analysis of RNA-related genomic features. BioMed. Res. Int. 2016, 8367534 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Zou, Y. et al. A comparative evaluation of computational models for RNA modification detection using nanopore sequencing with RNA004 chemistry. Brief. Bioinform. 26, bbaf404 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Loman, N. J., Quick, J. & Simpson, J. T. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods 12, 733–735 (2015).

    Article  CAS  PubMed  Google Scholar 

  56. Gamaarachchi, H. et al. GPU accelerated adaptive banded event alignment for rapid comparative nanopore signal analysis. BMC Bioinform. 21, 343 (2020).

    Article  CAS  Google Scholar 

  57. McCormick, C. A. et al. mRNA psi profiling using nanopore DRS reveals cell type-specific pseudouridylation. Preprint at bioRxiv https://doi.org/10.1101/2024.05.08.593203 (2024).

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (32270702 to J.S.) and the Fundamental Research Funds for the Central Universities (22120240023 to J.S.). We thank the members of the Shi Laboratory for their helpful discussions and suggestions throughout the project.

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Author notes

  1. These authors contributed equally: Tingting Luo, Moping Xu.

Authors and Affiliations

  1. Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital affiliated to Tongji University, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Tingting Luo, Moping Xu, Miao Wang, Faying Chen & Jiejun Shi

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  1. Tingting Luo
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Contributions

J.S. conceived and developed the outline of this research. T.L. and M.X. performed data analysis and method evaluations with the help from M.W. and F.C. T.L. and J.S. wrote the paper with the help from all other authors.

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Correspondence to Jiejun Shi.

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Nature Methods thanks Mattia Pelizzola and the other, anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–15.

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Supplementary Table 1 (download XLSX )

Overview of the original and retrained detection tools evaluated in this study. DRS datasets used for retraining and testing are labeled with ‘DS’ identifiers, which correspond to the detailed descriptions provided in Supplementary Table 2.

Supplementary Table 2 (download XLSX )

Summary of all DRS datasets used in this study.

Supplementary Table 3 (download XLSX )

Lists of RNA modification sites used as ground-truth references in this study.

Supplementary Table 4 (download XLSX )

Performance metrics for all evaluated detection tools.

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Luo, T., Xu, M., Wang, M. et al. Systematic evaluation of computational tools for multitype RNA modification detection using nanopore direct RNA sequencing. Nat Methods 23, 438–450 (2026). https://doi.org/10.1038/s41592-025-02974-y

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