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

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

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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.

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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.

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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.

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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

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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.

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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.

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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

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