Skip to main content

Thank you for visiting 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.

Detecting DNA cytosine methylation using nanopore sequencing


In nanopore sequencing devices, electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine (5-mC). Here we quantified the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. By using synthetically methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytosine. We applied our method to sequence the methylome of human DNA, without requiring special steps for library preparation.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Differences in event distribution between methylated and unmethylated 6-mers in nanopore sequencing data.
Figure 2: Methylation calling for human genomic DNA samples and controls.
Figure 3: Methylation changes in cancer cell lines.

Accession codes

Primary accessions

European Nucleotide Archive


  1. Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Nucleic Acids Res. 40, e136 (2012).

    CAS  Article  Google Scholar 

  2. Houseman, E.A. et al. BMC Bioinformatics 13, 86 (2012).

    Article  Google Scholar 

  3. Landan, G. et al. Nat. Genet. 44, 1207–1214 (2012).

    CAS  Article  Google Scholar 

  4. Flusberg, B.A. et al. Nat. Methods 7, 461–465 (2010).

    CAS  Article  Google Scholar 

  5. Beaulaurier, J. et al. Nat. Commun. 6, 7438 (2015).

    CAS  Article  Google Scholar 

  6. Clark, T.A. et al. BMC Biol. 11, 4 (2013).

    CAS  Article  Google Scholar 

  7. Hahn, M.A., Li, A.X., Wu, X. & Pfeifer, G.P. Methods Mol. Biol. 1238, 273–287 (2015).

    Article  Google Scholar 

  8. Laszlo, A.H. et al. Proc. Natl. Acad. Sci. USA 110, 18904–18909 (2013).

    CAS  Article  Google Scholar 

  9. Schreiber, J. et al. Proc. Natl. Acad. Sci. USA 110, 18910–18915 (2013).

    CAS  Article  Google Scholar 

  10. Loman, N.J., Quick, J. & Simpson, J.T. Nat. Methods 12, 733–735 (2015).

    CAS  Article  Google Scholar 

  11. Szalay, T. & Golovchenko, J.A. Nat. Biotechnol. 33, 1087–1091 (2015).

    CAS  Article  Google Scholar 

  12. Schreiber, J. & Karplus, K. Bioinformatics 31, 1897–1903 (2015).

    CAS  Article  Google Scholar 

  13. Timp, W., Comer, J. & Aksimentiev, A. Biophys. J. 102, L37–L39 (2012).

    CAS  Article  Google Scholar 

  14. Viner, C. et al. bioRxiv (2016).

  15. Ngo, T.T.M. et al. Nat. Commun. 7, 10813 (2016).

    CAS  Article  Google Scholar 

  16. Wescoe, Z.L., Schreiber, J. & Akeson, M. J. Am. Chem. Soc. 136, 16582–16587 (2014).

    CAS  Article  Google Scholar 

  17. Meyer, K.D. & Jaffrey, S.R. Genome Biol. 17, 5 (2016).

    Article  Google Scholar 

  18. Vogel, M.J., Peric-Hupkes, D. & van Steensel, B. Nat. Protoc. 2, 1467–1478 (2007).

    CAS  Article  Google Scholar 

  19. Kelly, T.K. et al. Genome Res. 22, 2497–2506 (2012).

    CAS  Article  Google Scholar 

  20. Meissner, A. et al. Nucleic Acids Res. 33, 5868–5877 (2005).

    CAS  Article  Google Scholar 

  21. Lee, E.-J. et al. Nucleic Acids Res. 39, e127 (2011).

    CAS  Article  Google Scholar 

  22. Krueger, F. & Andrews, S.R. Bioinformatics 27, 1571–1572 (2011).

    CAS  Article  Google Scholar 

  23. Hansen, K.D., Langmead, B. & Irizarry, R.A. Genome Biol. 13, R83 (2012).

    Article  Google Scholar 

  24. Quick, J. et al. Nature 530, 228–232 (2016).

    CAS  Article  Google Scholar 

Download references


We thank N. Loman and J. Quick for making the E. coli K12 data set publicly available, and A. Feinberg, K. Hansen and J. McPherson for helpful discussions. J.T.S., P.C.Z., M.D. and L.J.D. are supported by the Ontario Institute for Cancer Research through funds provided by the Government of Ontario. W.T. is supported in part by a Johns Hopkins University Catalyst award.

Author information

Authors and Affiliations



J.T.S. and W.T. designed the study; W.T., R.E.W. and P.C.Z. performed the experiments; J.T.S., M.D. and L.J.D. designed and implemented the methylation-training and inference framework; and J.T.S., W.T., R.E.W. and P.C.Z. wrote the paper.

Corresponding authors

Correspondence to Jared T Simpson or Winston Timp.

Ethics declarations

Competing interests

J.T.S. receives research funding from Oxford Nanopore Technologies, and W.T. has two patents licensed to Oxford Nanopore Technologies (US20110226623 A1 and US20120040342 A1). J.T.S. and W.T. have received travel funds to speak at symposia organized by Oxford Nanopore Technologies.

Integrated supplementary information

Supplementary Figure 1 Position dependence of methylation parameter shift.

A histogram of the difference between the trained mean for the methylated R7.3 (left column; PCR+M.SssI-R7.3-timp-021216) and R9 (right column; PCR+M.SssI-R9-timp-061716) datasets and the ONT reference model. Each row is the subset of k-mers with the methylated base in the first position (Mbcdef), the second position (aMcdef), and so on. We only included k-mers that contained a single methylation position. We restrict the plotting range to differences in the range -8 to 8 so some outliers are not shown.

Supplementary Figure 2 Methylation classifier error rate vs threshold.

Panel A shows the error rate of the methylated/unmethylated classifier as a function of the log likelihood ratio threshold required to make a call. In this analysis calls are only made at sites where the absolute value of the log likelihood ratio is greater than the threshold shown. Panel B shows the number of calls made as a function of this threshold.

Supplementary Figure 3 NA12878 CpG island methylation.

In the main panel each point is an annotated CpG island in the human genome that was covered by both bisulfite sequencing data and nanopore reads from the merged natural NA12878 DNA R7.3 data set. The x-coordinate of the point is the percentage of CpGs in the island that were predicted to be methylated from bisulfite data. The y-coordinate is the percentage of CpGs predicted to be methylated by our model using the nanopore data. The points are colored by whether the CGI is in a promoter(blue) or not(red). The histograms on the top and right of the figure are the marginal distributions of methylation percentages for the bisulfite and nanopore calls respectively.

Supplementary Figure 4 Cancer/Normal per CpG methylation analysis.

Comparison of bisulfite sequencing and nanopore R7.3 data in a cancer/normal reduced representation dataset. A) Correlation plot of per CpG methylation percentage from MCF10A bisulfite data (x-axis) versus nanopore data (y-axis), Pearson Correlation r=0.91. B) As in A) but for MDA-MB-231 samples, Pearson Correlation r=0.91.

Supplementary Figure 5 Log Likelihood Ratio comparison for human samples.

A histogram of the log likelihood ratios for unmethylated NA12878 DNA (top pane), methylated NA12878 DNA (middle pane) and natural NA12878 DNA (bottom pane). In these figures we only include singleton sites.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Note and Supplementary Tables 1–7 (PDF 1386 kb)

Supplementary Data 1

TSS by chromosomes (PDF 1931 kb)

Supplementary Data 3

Cancer Normal Regions (PDF 1128 kb)

Supplementary Data 4

Cancer-Normal Strand Data (PDF 984 kb)

Supplementary Data 2

CGI bisulfite vs nanopore (PDF 585 kb)

Supplementary Software (ZIP 8100 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Simpson, J., Workman, R., Zuzarte, P. et al. Detecting DNA cytosine methylation using nanopore sequencing. Nat Methods 14, 407–410 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


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