Analyzing Epigenome Data in Context of Genome Evolution and Human Diseases

  • Lars FeuerbachEmail author
  • Konstantin Halachev
  • Yassen Assenov
  • Fabian Müller
  • Christoph Bock
  • Thomas Lengauer
Part of the Methods in Molecular Biology book series (MIMB, volume 856)


This chapter describes bioinformatic tools for analyzing epigenome differences between species and in diseased versus normal cells. We illustrate the interplay of several Web-based tools in a case study of CpG island evolution between human and mouse. Starting from a list of orthologous genes, we use the Galaxy Web service to obtain gene coordinates for both species. These data are further analyzed in EpiGRAPH, a Web-based tool that identifies statistically significant epigenetic differences between genome region sets. Finally, we outline how the use of the statistical programming language R enables deeper insights into the epigenetics of human diseases, which are difficult to obtain without writing custom scripts. In summary, our tutorial describes how Web-based tools provide an easy entry into epigenome data analysis while also highlighting the benefits of learning a scripting language in order to unlock the vast potential of public epigenome datasets.

Key words

Epigenomics Computational epigenetics DNA methylation CpG islands Comparative genomics Galaxy EpiGRAPH R statistical programming language 



The contribution of Y.A. was partially supported by the EU STREP CancerDIP (EU grant HEALTH-F2-2007-200620)


  1. 1.
    Jaenisch, R., and Bird, A. (2003) Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals, Nat Genet 33 Suppl, 245–254.PubMedCrossRefGoogle Scholar
  2. 2.
    Bird, A. (2002) DNA methylation patterns and epigenetic memory, Genes Dev 16, 6–21.PubMedCrossRefGoogle Scholar
  3. 3.
    Novik, K. L., Nimmrich, I., Genc, B., Maier, S., Piepenbrock, C., Olek, A., and Beck, S. (2002) Epigenomics: genome-wide study of methylation phenomena, Current issues in molecular biology 4, 111–128-111–128.Google Scholar
  4. 4.
    Noushmehr, H., Weisenberger, D. J., Diefes, K., Phillips, H. S., Pujara, K., Berman, B. P., Pan, F., Pelloski, C. E., Sulman, E. P., Bhat, K. P., Verhaak, R. G., Hoadley, K. A., Hayes, D. N., Perou, C. M., Schmidt, H. K., Ding, L., Wilson, R. K., Van Den Berg, D., Shen, H., Bengtsson, H., Neuvial, P., Cope, L. M., Buckley, J., Herman, J. G., Baylin, S. B., Laird, P. W., and Aldape, K. (2010) Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma, Cancer Cell 17, 510–522.PubMedCrossRefGoogle Scholar
  5. 5.
    Figueroa, M. E., Lugthart, S., Li, Y., Erpelinck-Verschueren, C., Deng, X., Christos, P. J., Schifano, E., Booth, J., van Putten, W., Skrabanek, L., Campagne, F., Mazumdar, M., Greally, J. M., Valk, P. J., Lowenberg, B., Delwel, R., and Melnick, A. (2010) DNA methylation signatures identify biologically distinct subtypes in acute myeloid leukemia, Cancer Cell 17, 13–27.PubMedCrossRefGoogle Scholar
  6. 6.
    Yi, J. M., Dhir, M., Van Neste, L., Downing, S. R., Jeschke, J., Glockner, S. C., de Freitas Calmon, M., Hooker, C. M., Funes, J. M., Boshoff, C., Smits, K. M., van Engeland, M., Weijenberg, M. P., Iacobuzio-Donahue, C. A., Herman, J. G., Schuebel, K. E., Baylin, S. B., and Ahuja, N. (2011) Genomic and Epigenomic Integration Identifies a Prognostic Signature in Colon Cancer, Clin. Cancer Res. 17, 1535–1545.PubMedCrossRefGoogle Scholar
  7. 7.
    Bock, C., Kiskinis, E., Verstappen, G., Gu, H., Boulting, G., Smith, Z. D., Ziller, M., Croft, G. F., Amoroso, M. W., Oakley, D. H., Gnirke, A., Eggan, K., and Meissner, A. (2011) Reference Maps of Human ES and iPS Cell Variation Enable High-Throughput Characterization of Pluripotent Cell Lines, Cell 144, 439–452.PubMedCrossRefGoogle Scholar
  8. 8.
    Shen, J. C., Rideout III, W. M., and Jones, P. A. (1994) The rate of hydrolytic deamination of 5-methylcytosine in double-stranded DNA, Nucleic Acids Research 22, 972–972.PubMedCrossRefGoogle Scholar
  9. 9.
    Pfeifer, G. (2006) Mutagenesis at Methylated CpG Sequences, in DNA Methylation: Basic Mechanisms, pp 259–281.Google Scholar
  10. 10.
    Chahwan, R., Wontakal, S. N., and Roa, S. (2010) Crosstalk between genetic and epigenetic information through cytosine deamination, Trends in Genetics 26, 443–448.PubMedCrossRefGoogle Scholar
  11. 11.
    Weber, M., Hellmann, I., Stadler, M. B., Ramos, L., Paabo, S., Rebhan, M., and Schubeler, D. (2007) Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome, Nat Genet 39, 457–466.PubMedCrossRefGoogle Scholar
  12. 12.
    Bock, C., and Lengauer, T. (2008) Computational epigenetics, Bioinformatics 24, 1–10.PubMedCrossRefGoogle Scholar
  13. 13.
    Blankenberg, D., Kuster, G. V., Coraor, N., Ananda, G., Lazarus, R., Mangan, M., Nekrutenko, A., and Taylor, J. (2010) Galaxy: A Web-Based Genome Analysis Tool for Experimentalists, Vol. 89, John Wiley & Sons, Inc.Google Scholar
  14. 14.
    Bock, C., Halachev, K., Büch, J., and Lengauer, T. (2009) EpiGRAPH: User-friendly software for statistical analysis and prediction of (epi-) genomic data, Genome Biol 10, R14.PubMedCrossRefGoogle Scholar
  15. 15.
    Jiang, C., Han, L., Su, B., Li, W.-H., and Zhao, Z. (2007) Features and Trend of Loss of Promoter-Associated CpG Islands in the Human and Mouse Genomes, Molecular Biology and Evolution 24, 1991–2000.PubMedCrossRefGoogle Scholar
  16. 16.
    Bruford, E. A., Lush, M. J., Wright, M. W., Sneddon, T. P., Povey, S., and Birney, E. (2008) The HGNC Database in 2008: a resource for the human genome, Nucleic Acids Research 36, D445-D448.Google Scholar
  17. 17.
    Takai, D., and Jones, P. A. (2002) Comprehensive analysis of CpG islands in human chromosomes 21 and 22, Proc Natl Acad Sci USA 99, 3740–3745.PubMedCrossRefGoogle Scholar
  18. 18.
    Hastie, T., Tibshirani, R., and Friedman, J. H. (2001) The elements of statistical learning : data mining, inference, and prediction, Springer, New York.Google Scholar
  19. 19.
    Bock, C., Kuster, G. V., Halachev, K., Taylor, J., Nekrutenko, A., and Lengauer, T. (2009) Web-based analysis of (epi-) genome data using EpiGRAPH and Galaxy, Methods in Molecular Biology 628, 275–296.CrossRefGoogle Scholar
  20. 20.
    Matsuo, K., Clay, O., Takahashi, T., Silke, J., and Schaffner, W. (1993) Evidence for erosion of mouse CpG islands during mammalian evolution, Somat Cell Mol Genet 19, 543–555.PubMedCrossRefGoogle Scholar
  21. 21.
    Corder, G. W., and Foreman, D. I. (2009) Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach, John Wiley & Sons.Google Scholar
  22. 22.
    Shaffer, J. P. (1995) Multiple hypothesis testing, Annu. Rev. Psychol. 46, 561–584.CrossRefGoogle Scholar
  23. 23.
    Bock, C., Walter, J., Paulsen, M., and Lengauer, T. (2007) CpG island mapping by epigenome prediction, PLoS Comput Biol 3, e110.PubMedCrossRefGoogle Scholar
  24. 24.
    Gu, H., Bock, C., Mikkelsen, T. S., Jager, N., Smith, Z. D., Tomazou, E., Gnirke, A., Lander, E. S., and Meissner, A. (2010) Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution, Nat Meth 7, 133–136.CrossRefGoogle Scholar
  25. 25.
    Meissner, A., Mikkelsen, T. S., Gu, H., Wernig, M., Hanna, J., Sivachenko, A., Zhang, X., Bernstein, B. E., Nusbaum, C., Jaffe, D. B., Gnirke, A., Jaenisch, R., and Lander, E. S. (2008) Genome-scale DNA methylation maps of pluripotent and differentiated cells, Nature 454, 766–770.PubMedGoogle Scholar
  26. 26.
    Yoo, C. B., and Jones, P. A. (2006) Epigenetic therapy of cancer: past, present and future, Nat Rev Drug Discov 5, 37–50.PubMedCrossRefGoogle Scholar
  27. 27.
    Bibikova, M., Le, J., Barnes, B., Saedinia-Melnyk, S., Zhou, L., Shen, R., and Gunderson, K. L. (2009) Genome-wide DNA methylation profiling using Infinium assay, Epigenomics 1, 177–200.PubMedCrossRefGoogle Scholar
  28. 28.
    Weisenberger, D. J., Berg, D. V. D., Pan, F., Berman, B. P., and Laird, P. W. (2008) Comprehensive DNA Methylation Analysis on the Illumina Infinium Assay Platform [].
  29. 29.
    Teschendorff, A. E., Menon, U., Gentry-Maharaj, A., Ramus, S. J., Weisenberger, D. J., Shen, H., Campan, M., Noushmehr, H., Bell, C. G., Maxwell, A. P., Savage, D. A., Mueller-Holzner, E., Marth, C., Kocjan, G., Gayther, S. A., Jones, A., Beck, S., Wagner, W., Laird, P. W., Jacobs, I. J., and Widschwendter, M. (2010) Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer, Genome Research 20, 440–446.PubMedCrossRefGoogle Scholar
  30. 30.
    Eckhardt, F., Lewin, J., Cortese, R., Rakyan, V. K., Attwood, J., Burger, M., Burton, J., Cox, T. V., Davies, R., Down, T. A., Haefliger, C., Horton, R., Howe, K., Jackson, D. K., Kunde, J., Koenig, C., Liddle, J., Niblett, D., Otto, T., Pettett, R., Seemann, S., Thompson, C., West, T., Rogers, J., Olek, A., Berlin, K., and Beck, S. (2006) DNA methylation profiling of human chromosomes 6, 20 and 22, Nat Genet 38, 1378–1385.PubMedCrossRefGoogle Scholar
  31. 31.
    Besenbacher, S., Mailund, T., Schierup, M. (2012) Association mapping and disease: evolutionary perspectives. In Anisimova, M., (ed.), Evolutionary genomics: statistical and computational methods (volume 1). Methods in Molecular Biology, Springer Science+Business Media New York.Google Scholar
  32. 32.
    Altenhoff, A. M., Dessimoz, C. (2012) Inferring orthology and paralogy. In Anisimova, M., (ed.), Evolutionary genomics: statistical and computational methods (volume 1). Methods in Molecular Biology, Springer Science+Business Media New York.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Lars Feuerbach
    • 1
    Email author
  • Konstantin Halachev
    • 1
  • Yassen Assenov
    • 1
  • Fabian Müller
    • 1
    • 2
  • Christoph Bock
    • 1
    • 2
  • Thomas Lengauer
    • 1
  1. 1.Max Planck InstituteSaarbrückenGermany
  2. 2.Broad InstituteCambridgeUSA

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