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.

  • Analysis
  • Published:

Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications

Abstract

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: CpG coverage by each method.
Figure 2: Comparison of bisulfite-based methods.
Figure 3: Comparison of methylated DNA enrichment methods.
Figure 4: Comparison of all methods.
Figure 5: Integrative method increases methylome coverage and enables identification of a DMR.
Figure 6: Allelic DNA methylation, histone methylation and gene expression in ESCs.

Similar content being viewed by others

References

  1. Robertson, K.D. DNA methylation and human disease. Nat. Rev. Genet. 6, 597–610 (2005).

    Article  CAS  Google Scholar 

  2. Bird, A. DNA methylation patterns and epigenetic memory. Genes Dev. 16, 6–21 (2002).

    Article  CAS  Google Scholar 

  3. Feinberg, A.P. & Vogelstein, B. Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature 301, 89–92 (1983).

    Article  CAS  Google Scholar 

  4. Gama-Sosa, M.A. et al. Tissue-specific differences in DNA methylation in various mammals. Biochim. Biophys. Acta 740, 212–219 (1983).

    Article  CAS  Google Scholar 

  5. Tahiliani, M. et al. Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324, 930–935 (2009).

    Article  CAS  Google Scholar 

  6. Kriaucionis, S. & Heintz, N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324, 929–930 (2009).

    Article  CAS  Google Scholar 

  7. Ito, S. et al. Role of Tet proteins in 5mC to 5hmC conversion, ES-cell self-renewal and inner cell mass specification. Nature 466, 1129–1133 (2010).

    Article  CAS  Google Scholar 

  8. Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    Article  CAS  Google Scholar 

  9. Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766–770 (2008).

    Article  CAS  Google Scholar 

  10. Jacinto, F.V., Ballestar, E. & Esteller, M. Methyl-DNA immunoprecipitation (MeDIP): hunting down the DNA methylome. Biotechniques 44, 35–43 (2008).

    Article  CAS  Google Scholar 

  11. Down, T.A. et al. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat. Biotechnol. 26, 779–785 (2008).

    Article  CAS  Google Scholar 

  12. Serre, D., Lee, B.H. & Ting, A.H. MBD-isolated Genome Sequencing provides a high-throughput and comprehensive survey of DNA methylation in the human genome. Nucleic Acids Res. 38, 391–399 (2010).

    Article  CAS  Google Scholar 

  13. Maunakea, A.K. et al. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature 466, 253–257 (2010).

    Article  CAS  Google Scholar 

  14. Ball, M.P. et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nat. Biotechnol. 27, 361–368 (2009).

    Article  CAS  Google Scholar 

  15. Cokus, S.J. et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215–219 (2008).

    Article  CAS  Google Scholar 

  16. Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008).

    Article  CAS  Google Scholar 

  17. The American Association for Cancer Research Human Epigenome Task Force European Union, Network of Excellence, Scientific Advisory Board Moving AHEAD with an international human epigenome project. Nature 454, 711–715 (2008).

  18. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  19. Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).

    Article  Google Scholar 

  20. Coarfa, C. & Milosavljevic, A. Pash 2.0: scaleable sequence anchoring for next-generation sequencing technologies. Pac. Symp. Biocomput. 2008, 102–113 (2008).

    Google Scholar 

  21. Smith, A.D. et al. Updates to the RMAP short-read mapping software. Bioinformatics 25, 2841–2842 (2009).

    Article  CAS  Google Scholar 

  22. Lin, H., Zhang, Z., Zhang, M.Q., Ma, B. & Li, M. ZOOM! Zillions of oligos mapped. Bioinformatics 24, 2431–2437 (2008).

    Article  CAS  Google Scholar 

  23. Wang, T. et al. Species-specific endogenous retroviruses shape the transcriptional network of the human tumor suppressor protein p53. Proc. Natl. Acad. Sci. USA 104, 18613–18618 (2007).

    Article  CAS  Google Scholar 

  24. Kunarso, G. et al. Transposable elements have rewired the core regulatory network of human embryonic stem cells. Nat. Genet. 42, 631–634 (2010).

    Article  CAS  Google Scholar 

  25. Pant, P.V.K. et al. Analysis of allelic differential expression in human white blood cells. Genome Res. 16, 331–339 (2006).

    Article  CAS  Google Scholar 

  26. Pollard, K.S. et al. A genome-wide approach to identifying novel-imprinted genes. Hum. Genet. 122, 625–634 (2008).

    Article  CAS  Google Scholar 

  27. Schalkwyk, L.C. et al. Allelic skewing of DNA methylation is widespread across the genome. Am. J. Hum. Genet. 86, 196–212 (2010).

    Article  CAS  Google Scholar 

  28. Pick, M. et al. Clone- and gene-specific aberrations of parental imprinting in human induced pluripotent stem cells. Stem Cells 27, 2686–2690 (2009).

    Article  CAS  Google Scholar 

  29. Arnaud, P. et al. Conserved methylation imprints in the human and mouse GRB10 genes with divergent allelic expression suggests differential reading of the same mark. Hum. Mol. Genet. 12, 1005–1019 (2003).

    Article  CAS  Google Scholar 

  30. Li, N. et al. Whole genome DNA methylation analysis based on high throughput sequencing technology. Methods published online, doi: 10.1016/j.ymeth.2010.04.009 (27 April 2010).

  31. Deng, J. et al. Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming. Nat. Biotechnol. 27, 353–360 (2009).

    Article  CAS  Google Scholar 

  32. Bourque, G. Transposable elements in gene regulation and in the evolution of vertebrate genomes. Curr. Opin. Genet. Dev. 19, 607–612 (2009).

    Article  CAS  Google Scholar 

  33. Duhl, D.M., Vrieling, H., Miller, K.A., Wolff, G.L. & Barsh, G.S. Neomorphic agouti mutations in obese yellow mice. Nat. Genet. 8, 59–65 (1994).

    Article  CAS  Google Scholar 

  34. Waterland, R.A. & Jirtle, R.L. Transposable elements: targets for early nutritional effects on epigenetic gene regulation. Mol. Cell. Biol. 23, 5293–5300 (2003).

    Article  CAS  Google Scholar 

  35. Hellman, A. & Chess, A. Gene body-specific methylation on the active X chromosome. Science 315, 1141–1143 (2007).

    Article  CAS  Google Scholar 

  36. Ludwig, T.E. et al. Feeder-independent culture of human embryonic stem cells. Nat. Methods 3, 637–646 (2006).

    Article  CAS  Google Scholar 

  37. Gu, H. et al. Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat. Methods 7, 133–136 (2010).

    Article  CAS  Google Scholar 

  38. Smith, Z.D., Gu, H., Bock, C., Gnirke, A. & Meissner, A. High-throughput bisulfite sequencing in mammalian genomes. Methods 48, 226–232 (2009).

    Article  CAS  Google Scholar 

  39. O'Geen, H., Frietze, S. & Farnham, P.J. Using ChIP-seq technology to identify targets of zinc finger transcription factors. Methods Mol. Biol. 649, 437–455 (2010).

    Article  CAS  Google Scholar 

  40. Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).

    Article  CAS  Google Scholar 

  41. Blahnik, K.R. et al. Sole-Search: an integrated analysis program for peak detection and functional annotation using ChIP-seq data. Nucleic Acids Res. 38, e13 (2010).

    Article  Google Scholar 

  42. Waterland, R.A., Lin, J., Smith, C.A. & Jirtle, R.L. Post-weaning diet affects genomic imprinting at the insulin-like growth factor 2 (Igf2) locus. Hum. Mol. Genet. 15, 705–716 (2006).

    Article  CAS  Google Scholar 

  43. Shen, L., Guo, Y., Chen, X., Ahmed, S. & Issa, J.J. Optimizing annealing temperature overcomes bias in bisulfite PCR methylation analysis. Biotechniques 42, 48, 50, 52 passim (2007).

  44. Grunau, C., Clark, S.J. & Rosenthal, A. Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res 29, E65 (2001).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank the US National Institutes of Health (NIH) Roadmap Epigenomics Program; sponsored by the National Institute on Drug Abuse (NIDA) and the National Institute of Environmental Health Sciences (NIEHS). J.F.C. and M.H. are supported by NIH grant 5U01ES017154-02. A. Milosavljevic is supported by NIH grant 5U01DA025956-02. A. Meissner and B.E.B. are supported by NIH grant 6U01ES017155-02. J.R.E. and B.R. are supported by NIH grant 5U01ES017166-02. R.P.N. was supported by NIH T32 CA108462-04 and F32CA141799. S.L.D. was supported by CIRM TB1-01190. S.D.F. was supported by NIH T32 CA108462-06. B.E.J. was supported by NIH T32 GM008568. M.A.M. is a Terry Fox Young Investigator and a Michael Smith Senior Research Scholar. We thank Z. Zhang and H. Li for modifying the ZOOM algorithm for bisulfite alignments.

Author information

Authors and Affiliations

Authors

Contributions

J.F.C., R.A.H., T.W., M.H., M.A.M. and A. Milosavljevic conceived and designed the experiments. R.P.N., C.H., S.L.D., B.E.J., S.D.F., Y.Z. and M.H. performed the MeDIP, MRE and bisulfite sequencing experiments. R.A.W. and X.Z. designed and performed pyrosequencing and data analyses. H.G., C.B., A.G. and A. Meissner9 performed and analyzed RRBS. L.E., H.O., P.J.F., B.E.B., C.B.E., R.D.H. and B.R. performed and analyzed Chip-seq experiments. R.L., M.P. and J.R.E. analyzed MethylC-seq data and performed Bowtie aligner testing. R.A.H., T.W., K.J.F., J.G., C.C., M.H., X.Z., A.D. and A.O. performed data analysis. T.W., T.B. and D.H. developed MeDIP and methyl-sensitive restriction enzyme scoring algorithms and performed coverage analyses including repetitive sequence analyses. Y.X., W.-Y.C., R.L., M.Q.Z. and W.L. compared bisulfite sequence aligners. J.F.C., R.A.H., M.H., T.W., R.P.N. and R.A.W. wrote the manuscript.

Corresponding author

Correspondence to Joseph F Costello.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 2, 4, 5 and 8 and Supplementary Figs. 1–18 (PDF 3818 kb)

Supplementary Table 1

Primer designs for bisulfite pyrosequencing. See Excel spreadsheet Supplementary_Table_1.xls. (XLS 33 kb)

Supplementary Table 3

Bisulfite data for Supplementary Figure 12. (XLS 118 kb)

Supplementary Table 6

Genome-wide catalogue of CpG island regions exhibiting overlapping MeDIP-seq (methylated) signals and MRE-seq (unmethylated) signals. (XLS 223 kb)

Supplementary Table 7

Validation of known and putative DMRs by bisulfite, PCR, cloning and sequencing. (XLS 250 kb)

Supplementary Table 9

Details of the comparison of genomic variation between pairs of assays to determine allele-specific epigenetic states. (XLS 409 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Harris, R., Wang, T., Coarfa, C. et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat Biotechnol 28, 1097–1105 (2010). https://doi.org/10.1038/nbt.1682

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1682

This article is cited by

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