Advertisement

MethCP: Differentially Methylated Region Detection with Change Point Models

  • Boying Gong
  • Elizabeth PurdomEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11467)

Abstract

Whole-genome bisulfite sequencing (WGBS) provides a precise measure of methylation across the genome, yet presents a challenge in identifying regions that are differentially methylated (DMRs) between different conditions. Many methods have been developed, which focus primarily on the setting of two-group comparison. We develop a DMR detecting method MethCP for WGBS data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. For simple two-group comparison, we show that our method outperforms developed methods in accurately detecting the complete DM region on a simulated dataset and an Arabidopsis dataset. Moreover, we show that MethCP is capable of detecting wide regions with small effect sizes, which can be common in some settings but existing techniques are poor in detecting such DMRs. We also demonstrate the use of MethCP for time-course data on another dataset following methylation throughout seed germination in Arabidopsis.

Availability: The package MethCP has been submitted to Bioconductor, and is currently available at https://github.com/boyinggong/MethCP.

Keywords

Differential methylation Bisulfite sequencing Change point detection 

Notes

Funding

This work has been supported in part by a DOE BER grant, DE-SC0014081.

Supplementary material

References

  1. 1.
    Akalin, A., et al.: MethylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13(10), R87 (2012)Google Scholar
  2. 2.
    Borenstein, M., Hedges, L.V., Higgins, J., Rothstein, H.R.: Introduction to Meta-Analysis. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  3. 3.
    Breton, C.V., et al.: Small-magnitude effect sizes in epigenetic end points are important in children’s environmental health studies: the children’s environmental health and disease prevention research centers epigenetics working group. Environ. Health Perspect. 125(4), 511 (2017)Google Scholar
  4. 4.
    Coleman-Derr, D., Zilberman, D.: Deposition of histone variant H2a. Z within gene bodies regulates responsive genes. PLoS Genet. 8(10), e1002988 (2012)Google Scholar
  5. 5.
    Cruickshanks, H.A., et al.: Senescent cells harbour features of the cancer epigenome. Nat. Cell Biol. 15(12), 1495 (2013)Google Scholar
  6. 6.
    Dolzhenko, E., Smith, A.D.: Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinform. 15(1), 215 (2014)Google Scholar
  7. 7.
    Eichten, S.R., Springer, N.M.: Minimal evidence for consistent changes in maize DNA methylation patterns following environmental stress. Front. Plant Sci. 6, 308 (2015)Google Scholar
  8. 8.
    Feng, H., Conneely, K.N., Wu, H.: A Bayesian hierarchical model to detect differentially methylated loci from single nucleotide resolution sequencing data. Nucleic Acids Res. 42(8), e69 (2014)Google Scholar
  9. 9.
    Fisher, R.A.: Statistical methods for research workers (1934)Google Scholar
  10. 10.
    Hansen, K.D., Langmead, B., Irizarry, R.A.: Bsmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13(10), R83 (2012)Google Scholar
  11. 11.
    Hebestreit, K., Dugas, M., Klein, H.-U.: Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29(13), 1647–1653 (2013)CrossRefGoogle Scholar
  12. 12.
    Huang, Q., Dom, B.: Quantitative methods of evaluating image segmentation. In: Proceedings of International Conference on Image Processing, vol. 3, pp. 53–56. IEEE (1995)Google Scholar
  13. 13.
    Jühling, F., Kretzmer, H., Bernhart, S.H., Otto, C., Stadler, P.F., Hoffmann, S.: metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data. Genome Res. 26(2), 256–262 (2016)Google Scholar
  14. 14.
    Kawakatsu, T., Nery, J.R., Castanon, R., Ecker, J.R.: Dynamic DNA methylation reconfiguration during seed development and germination. Genome Biol. 18(1), 171 (2017)Google Scholar
  15. 15.
    Leenen, F.A.D., Muller, C.P., Turner, J.D.: DNA methylation: conducting the orchestra from exposure to phenotype? Clin. Epigenetics 8(1), 92 (2016)Google Scholar
  16. 16.
    Olshen, A.B., Venkatraman, E.S., Lucito, R., Wigler, M.: Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5(4), 557–572 (2004)Google Scholar
  17. 17.
    Park, Y., Hao, W.: Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics 32(10), 1446–1453 (2016)CrossRefGoogle Scholar
  18. 18.
    Pont-Tuset, J., Marques, F.: Supervised evaluation of image segmentation and object proposal techniques. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1465–1478 (2016)CrossRefGoogle Scholar
  19. 19.
    Shafi, A., Mitrea, C., Nguyen, T., Draghici, S.: A survey of the approaches for identifying differential methylation using bisulfite sequencing data. Briefings Bioinform. 19, 737–753 (2017)Google Scholar
  20. 20.
    Stouffer, S.A., Suchman, E.A., DeVinney, L.C., Star, S.A., Williams Jr., R.M.: The American Soldier: Adjustment During Army Life. (Studies in Social Psychology in World War II), vol. 1 (1949)Google Scholar
  21. 21.
    Sun, S., Yu, X.: HMM-Fisher: identifying differential methylation using a hidden Markov model and fisher’s exact test. Stat. Appl. Genet. Mol. Biol. 15(1), 55–67 (2016)Google Scholar
  22. 22.
    Teschendorff, A.E., Relton, C.L.: Statistical and integrative system-level analysis of DNA methylation data. Nat. Rev. Genet. 19(3), 129 (2018)Google Scholar
  23. 23.
    Whitlock, M.C.: Combining probability from independent tests: the weighted Z-method is superior to fisher’s approach. J. Evol. Biol. 18(5), 1368–1373 (2005)Google Scholar
  24. 24.
    Wu, H., et al.: Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates. Nucleic Acids Res. 43(21), e141 (2015)Google Scholar
  25. 25.
    Xiaoqing, Y., Sun, S.: Comparing five statistical methods of differential methylation identification using bisulfite sequencing data. Stat. Appl. Genet. Mol. Biol. 15(2), 173–191 (2016)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Xiaoqing, Y., Sun, S.: HMM-DM: identifying differentially methylated regions using a hidden Markov model. Stat. Appl. Genet. Mol. Biol. 15(1), 69–81 (2016)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Division of BiostatisticsUniversity of California, BerkeleyBerkeleyUSA
  2. 2.Department of StatisticsUniversity of California, BerkeleyBerkeleyUSA

Personalised recommendations