Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore

Nature Biotechnology volume 39, pages 1394–1402 (2021)Cite this article

Subjects

Abstract

RNA modifications, such as N6-methyladenosine (m6A), modulate functions of cellular RNA species. However, quantifying differences in RNA modifications has been challenging. Here we develop a computational method, xPore, to identify differential RNA modifications from nanopore direct RNA sequencing (RNA-seq) data. We evaluate our method on transcriptome-wide m6A profiling data, demonstrating that xPore identifies positions of m6A sites at single-base resolution, estimates the fraction of modified RNA species in the cell and quantifies the differential modification rate across conditions. We apply xPore to direct RNA-seq data from six cell lines and multiple myeloma patient samples without a matched control sample and find that many m6A sites are preserved across cell types, whereas a subset exhibit significant differences in their modification rates. Our results show that RNA modifications can be identified from direct RNA-seq data with high accuracy, enabling analysis of differential modifications and expression from a single high-throughput experiment.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic workflow: quantification of RNA modifications from direct RNA-seq data using xPore.
Fig. 2: Detection of m6A sites in the human transcriptome.
Fig. 3: xPore modification-rate estimates correspond to the fraction of modified RNA species in the cell.
Fig. 4: Transcriptome-wide identification of differentially modified positions.
Fig. 5: Identification of m6A sites across different tissues and cell lines.
Fig. 6: Identification of m6A in clinical samples using direct RNA-seq.

Data availability

All data generated in this study are publicly available through the ENA (PRJEB40872). Here we used the following samples: HEK293T METTL3-KO cells (three replicates); HEK293T WT cells (three replicates); HEK293T METTL3-KD cells (three replicates); HEK293T KD control WT cells (three replicates); HEK293T WT–KO mixture, 100% modified (three replicates); HEK293T WT–KO mixture, 75% modified (four replicates); HEK293T WT–KO mixture, 50% modified (four replicates); HEK293T WT–KO mixture, 20% modified (four replicates); HEK293T WT–KO mixture, 0% modified (three replicates); multiple myeloma patient samples (three patient samples).

In addition, we used direct RNA-seq data from the SG-NEx project33, which are available at https://github.com/GoekeLab/sg-nex-data and https://www.ebi.ac.uk/ena/browser/view/PRJEB44348.

Preprocessed files for all samples are available at https://doi.org/10.5281/zenodo.4604945 for SG-NEx data and https://doi.org/10.5281/zenodo.4587661 for the other samples, which can be directly used to identify differential RNA modifications with xPore. The list of all samples can be found in Supplementary Data 7.

Samples from the three patients with myeloma were obtained after informed consent. The use of these samples for research was approved by the Domain Specific Review Board in Singapore.

Code availability

Our implementation in Python is available at https://github.com/GoekeLab/xpore. xPore’s documentation is available at https://xpore.readthedocs.io.

References

  1. Zheng, G. et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell 49, 18–29 (2013).

    Article  CAS  Google Scholar 

  2. Wang, Y. et al. N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014).

    Article  CAS  Google Scholar 

  3. Zhao, X. et al. FTO-dependent demethylation of N6-methyladenosine regulates mRNA splicing and is required for adipogenesis. Cell Res. 24, 1403–1419 (2014).

    Article  CAS  Google Scholar 

  4. Geula, S. et al. Stem cells. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 347, 1002–1006 (2015).

    Article  CAS  Google Scholar 

  5. Chen, T. et al. m6A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell 16, 289–301 (2015).

    Article  Google Scholar 

  6. Xu, K. et al. Mettl3-mediated m6A regulates spermatogonial differentiation and meiosis initiation. Cell Res. 27, 1100–1114 (2017).

    Article  CAS  Google Scholar 

  7. Mathiyalagan, P. et al. FTO-dependent N6-methyladenosine regulates cardiac function during remodeling and repair. Circulation 139, 518–532 (2019).

    Article  CAS  Google Scholar 

  8. Li, Z. et al. FTO plays an oncogenic role in acute myeloid leukemia as a N6-methyladenosine RNA demethylase. Cancer Cell 31, 127–141 (2017).

    Article  Google Scholar 

  9. Su, R. et al. R-2HG exhibits anti-tumor activity by targeting FTO/mA/MYC/CEBPA signaling. Cell 172, 90–105 (2018).

    Article  CAS  Google Scholar 

  10. Deng, X. et al. RNA N6-methyladenosine modification in cancers: current status and perspectives. Cell Res. 28, 507–517 (2018).

    Article  CAS  Google Scholar 

  11. Wang, X. et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

    Article  Google Scholar 

  12. Meyer, K. D. et al. 5′ UTR m6A promotes cap-independent translation. Cell 163, 999–1010 (2015).

    Article  CAS  Google Scholar 

  13. Alarcón, C. R., Lee, H., Goodarzi, H., Halberg, N. & Tavazoie, S. F. N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482–485 (2015).

    Article  Google Scholar 

  14. Alarcón, C. R. et al. HNRNPA2B1 is a mediator of m6A-dependent nuclear RNA processing events. Cell 162, 1299–1308 (2015).

    Article  Google Scholar 

  15. Koh, C. W. Q., Goh, Y. T. & Goh, W. S. S. Atlas of quantitative single-base-resolution N6-methyl-adenine methylomes. Nat. Commun. 10, 5636 (2019).

  16. Zaccara, S. & Jaffrey, S. R. A unified model for the function of YTHDF proteins in regulating m6A-modified mRNA. Cell 181, 1582–1595 (2020).

    Article  CAS  Google Scholar 

  17. Goh, Y. T., Koh, C. W. Q., Sim, D. Y., Roca, X. & Goh, W. S. S. METTL4 catalyzes m6Am methylation in U2 snRNA to regulate pre-mRNA splicing. Nucleic Acids Res. 48, 9250–9261 (2020).

  18. Yankova, E. et al. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia. Nature 593, 597–601 (2021).

    Article  CAS  Google Scholar 

  19. Zaccara, S., Ries, R. J. & Jaffrey, S. R. Reading, writing and erasing mRNA methylation. Nat. Rev. Mol. Cell Biol. 20, 608–624 (2019).

    Article  CAS  Google Scholar 

  20. Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).

    Article  CAS  Google Scholar 

  21. Meyer, K. D. DART-seq: an antibody-free method for global m6A detection. Nat. Methods 16, 1275–1280 (2019).

    Article  CAS  Google Scholar 

  22. Garalde, D. R. et al. Highly parallel direct RNA sequencing on an array of nanopores. Nat. Methods 15, 201–206 (2018).

    Article  CAS  Google Scholar 

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

  24. Leger, A. et al. RNA modifications detection by comparative Nanopore direct RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/843136 (2019).

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

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  28. 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  Google Scholar 

  29. Simpson, J. T. et al. Detecting DNA cytosine methylation using nanopore sequencing. Nat. Methods 14, 407–410 (2017).

    Article  CAS  Google Scholar 

  30. Corduneanu, A. & Bishop, C. M. Variational Bayesian model selection for mixture distributions. in Proceedings of the Eighth International Conference on Artificial Intelligence and Statistics 27–34 (Morgan Kaufmann, 2001).

  31. 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  Google Scholar 

  32. Liu, N. et al. Probing N6-methyladenosine RNA modification status at single nucleotide resolution in mRNA and long noncoding RNA. RNA 19, 1848–1856 (2013).

    Article  CAS  Google Scholar 

  33. Chen, Y. et al. A systematic benchmark of Nanopore long read RNA sequencing for transcript level analysis in human cell lines. Preprint at bioRxiv https://doi.org/10.1101/2021.04.21.440736 (2021).

  34. McIntyre, A. B. R. et al. Limits in the detection of m6A changes using MeRIP/m6A-seq. Sci. Rep. 10, 6590 (2020).

    Article  CAS  Google Scholar 

  35. Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).

    Article  CAS  Google Scholar 

  36. Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).

    Article  CAS  Google Scholar 

  37. Ke, S. et al. A majority of m6A residues are in the last exons, allowing the potential for 3′ UTR regulation. Genes Dev. 29, 2037–2053 (2015).

    Article  CAS  Google Scholar 

  38. Garcia-Campos, M. A. et al. Deciphering the ‘m6A code’ via antibody-independent quantitative profiling. Cell 178, 731–747 (2019).

    Article  CAS  Google Scholar 

  39. Shu, X. et al. A metabolic labeling method detects m6A transcriptome-wide at single base resolution. Nat. Chem. Biol. 16, 887–895 (2020).

  40. Ueda, H. nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification. Preprint at bioRxiv https://doi.org/10.1101/2020.09.13.295089 (2020).

  41. Ding, H., Bailey, A. D., Jain, M., Olsen, H. & Paten, B. Gaussian mixture model-based unsupervised nucleotide modification number detection using nanopore-sequencing readouts. Bioinformatics 36, 4928–4934 (2020).

    Article  CAS  Google Scholar 

  42. 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  Google Scholar 

  43. Kim, D. et al. The architecture of SARS-CoV-2 transcriptome. Cell 181, 914–921 (2020).

  44. Workman, R. E. et al. Nanopore native RNA sequencing of a human poly(A) transcriptome. Nat. Methods 16, 1297–1305 (2019).

    Article  CAS  Google Scholar 

  45. Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    Article  CAS  Google Scholar 

  46. Grozhik, A. V., Linder, B., Olarerin-George, A. O. & Jaffrey, S. R. Mapping m6A at individual-nucleotide resolution using crosslinking and immunoprecipitation (miCLIP). Methods Mol. Biol. 1562, 55–78 (2017).

Download references

Acknowledgements

This work is funded by the Agency for Science, Technology and Research (A∗STAR), Singapore and by the Singapore Ministry of Health’s National Medical Research Council under its Individual Research Grant funding scheme. P.N.P. acknowledges the Thailand Research Fund under grant number RTA6080013. We thank the Lezhava laboratory for assistance with sequencing. We thank M. Shee Siok Woon for help with administrative support.

Author information

Author notes

  1. These authors contributed equally: Fei Yao, Ying Chen, Casslynn W. Q. Koh, Yuk Kei Wan.

Authors and Affiliations

  1. Genome Institute of Singapore, A*STAR, Singapore, Singapore

    Ploy N. Pratanwanich, Fei Yao, Ying Chen, Casslynn W. Q. Koh, Yuk Kei Wan, Christopher Hendra, Polly Poon, Yeek Teck Goh, Phoebe M. L. Yap, Sarah B. Ng, W. S. Sho Goh & Jonathan Göke

  2. Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, Thailand

    Ploy N. Pratanwanich

  3. Chula Intelligent and Complex Systems Research Unit, Chulalongkorn University, Bangkok, Thailand

    Ploy N. Pratanwanich

  4. Institute of Data Science, National University of Singapore, Singapore, Singapore

    Christopher Hendra

  5. Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore

    Jing Yuan Chooi & Wee Joo Chng

  6. NUS Center for Cancer Research and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Wee Joo Chng

  7. Department of Haematology–Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore

    Wee Joo Chng

  8. Department of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore

    Alexandre Thiery

  9. Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, China

    W. S. Sho Goh

  10. National Cancer Centre Singapore, Singapore, Singapore

    Jonathan Göke

Authors
  1. Ploy N. Pratanwanich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Casslynn W. Q. Koh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuk Kei Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christopher Hendra
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Polly Poon
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yeek Teck Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Phoebe M. L. Yap
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jing Yuan Chooi
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wee Joo Chng
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Sarah B. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Alexandre Thiery
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. W. S. Sho Goh
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jonathan Göke
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.N.P. designed and implemented the computational method. J.G. and W.S.S.G. conceived the project. P.N.P., J.G. and W.S.S.G. designed the study and experiments and analyzed data. A.T. contributed to design of the computational method. F.Y., Y.C., C.W.Q.K., Y.K.W., C.H., P.P., Y.T.G., P.M.L.Y., J.Y.C., W.J.C. and S.B.N. contributed to data generation, data processing and data interpretation. Y.K.W. contributed to implementation of the computational method. P.N.P., W.S.S.G. and J.G. organized and wrote the paper with contributions from all authors.

Corresponding authors

Correspondence to Ploy N. Pratanwanich, W. S. Sho Goh or Jonathan Göke.

Ethics declarations

Competing interests

W.S.S.G. has filed a technology disclosure to the institutional technology transfer office, and the office has filed a provisional patent application in Singapore on the use of photo-crosslinking RNA-modification-specific antibodies and exoribonucleases to sequence RNA modifications at high resolution. All other authors have no competing interests.

Additional information

Peer review information Nature Biotechnology thanks Angus Wilson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9 and Supplementary Text.

Reporting Summary

Supplementary Data 1

xPore output containing significantly differentially modified sites between HEK293T KO and HEK293T WT (P < 0.05) cells. P values were calculated from two-tailed, unpooled z-tests on modification-rate differences and adjusted for multiple comparisons using the Benjamini–Hochberg procedure.

Supplementary Data 2

xPore output containing all sites tested in mixture RNA samples. P values were calculated from two-tailed, unpooled z-tests on modification-rate differences and adjusted for multiple comparisons using the Benjamini–Hochberg procedure.

Supplementary Data 3

xPore output containing all sites tested in HEK293T KO, HEK293T KD and HEK293T WT samples. P values were calculated from two-tailed, unpooled z-tests on modification-rate differences and adjusted for multiple comparisons using the Benjamini–Hochberg procedure.

Supplementary Data 4

xPore output containing m6A sites across the six cell lines. P values were calculated from two-tailed, unpooled z-tests on modification-rate differences and adjusted for multiple comparisons using the Benjamini–Hochberg procedure.

Supplementary Data 5

xPore output containing significantly differentially modified NNANN sites in multiple myeloma samples (P < 0.05). P values were calculated from two-tailed, unpooled z-tests on modification-rate differences and adjusted for multiple comparisons using the Benjamini–Hochberg procedure.

Supplementary Data 6

Column description for all tables.

Supplementary Data 7

Sample description.

Supporting Data Supplementary Fig. 9

Unprocessed western blots.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pratanwanich, P.N., Yao, F., Chen, Y. et al. Identification of differential RNA modifications from nanopore direct RNA sequencing with xPore. Nat Biotechnol 39, 1394–1402 (2021). https://doi.org/10.1038/s41587-021-00949-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-021-00949-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing