Journal of Computer Science and Technology

, Volume 25, Issue 1, pp 26–34 | Cite as

Challenges in Understanding Genome-Wide DNA Methylation

Survey

Abstract

DNA methylation is a chemical modification of the bases in genomes. This modification, most frequently found at CpG dinucleotides in eukaryotes, has been identified as having multiple critical functions in broad and diverse species of animals and plants, while mysteriously appears to be lacking from several other well-studied species. DNA methylation has well known and important roles in genome stability and defense, its pattern change highly correlates with gene regulation. Much evidence has linked abnormal DNA methylation to human diseases. Most prominently, aberrant DNA methylation is a common feature of cancer genomes. Elucidating the precise functions of DNA methylation therefore has great biomedical significance. Here we provide an update on large-scale experimental technologies for detecting DNA methylation on a genomic scale. We also discuss new prospect and challenges that computational biologist will face when analyzing DNA methylation data.

Keywords

DNA methylation epigenome computational epigenomics 

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References

  1. [1]
    Schwartz D C, Waterman M S. New generations: Sequencing machines and their computational challenges. J. Comput. Sci. & Technol., 2010, 25(1): 3-9.CrossRefGoogle Scholar
  2. [2]
    Holliday R, Pugh J E. DNA modification mechanisms and gene activity during development. Science, 1975, 187(4173): 226-232.CrossRefGoogle Scholar
  3. [3]
    Riggs A. X inactivation, differentiation, and DNA methylation. Cytogenet. Cell. Genet., 1975, 14(1): 9-25.CrossRefGoogle Scholar
  4. [4]
    Bird A. DNA methylation patterns and epigenetic memory. Genes & Development, 2002, 16(1): 6-21.CrossRefMathSciNetGoogle Scholar
  5. [5]
    Bestor T H. The DNA methyltransferases of mammals. Human Molecular Genetics, 2000, 9(16): 2395-2402.CrossRefGoogle Scholar
  6. [6]
    Yoder J A, Walsh C P, Bestor T H. Cytosine methylation and the ecology of intragenomic parasites. Trends in Genetics, 1997, 13(8): 335-340.CrossRefGoogle Scholar
  7. [7]
    Bestor T H. Cytosine methylation mediates sexual conflict. Trends in Genetics, 2003, 19(4): 185-190.CrossRefGoogle Scholar
  8. [8]
    Gonzalgo M L, Jones P A. Rapid quantitation of methylation differences at specific sites using methylation-sensitive single nucleotide primer extension (Ms-SNuPE). Nucleic Acids Research, 1997, 25(12): 2529-2531.CrossRefGoogle Scholar
  9. [9]
    Simmen M W. Genome-scale relationships between cytosine methylation and dinucleotide abundances in animals. Genomics, 2008, 92(1): 33-40.CrossRefGoogle Scholar
  10. [10]
    Cooper D N, Youssoufian H. The CpG dinucleotide and human genetic disease. Human Genetics, 1988, 78(2): 151-155.CrossRefGoogle Scholar
  11. [11]
    Jiang C, Zhao Z. Mutational spectrum in the recent human genome inferred by single nucleotide polymorphisms. Genomics, 2006, 88(5): 527-534.CrossRefMathSciNetGoogle Scholar
  12. [12]
    Wood L D, Parsons D W, Jones S et al. The genomic landscapes of human breast and colorectal cancers. Science, 2007, 318(5853): 1108-1113.CrossRefGoogle Scholar
  13. [13]
    Human Epigenome Consortium. http://www.epigenome.org/, Accessed Sept. 16, 2009.
  14. [14]
    Epigenomics — Overview. Division of Program Coordination, Planning, and Strategic Initiatives, National Institutes of Healt. http://nihroadmap.nih.gov/epigenomics/, Accessed Sept. 16, 2009.
  15. [15]
    Raleigh E A. Organization and function of the mcrBC genes of Escherichia coli K-12. Molecular Microbiology, 1992, 6(9): 1079-1086.CrossRefGoogle Scholar
  16. [16]
    Bird A P. Use of restriction enzymes to study eukaryotic DNA methylation: II. The symmetry of methylated sites supports semi-conservative copying of the methylation pattern. Journal of Molecular Biology, 1978, 118(1): 49-60.CrossRefMathSciNetGoogle Scholar
  17. [17]
    Gruenbaum Y, Cedar H, Razin A. Restriction enzyme digestion of hemimethylated DNA. Nucl. Acids Res., 1981, 9(11): 2509-2515.CrossRefGoogle Scholar
  18. [18]
    Lippman Z, Gendrel A V, Colot V, Martienssen R. Profiling DNA methylation patterns using genomic tiling microarrays. Nature Methods, 2005, 2(3): 219-224.CrossRefGoogle Scholar
  19. [19]
    Weber M, Davies J J, Wittig D, Oakeley E J, Haase M, Lam W L, Schubeler D. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet., 2005, 37(8): 853-862.CrossRefGoogle Scholar
  20. [20]
    Down T A, Rakyan V K, Turner D J et al. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat. Biotech., 2008, 26(7): 779-785.CrossRefGoogle Scholar
  21. [21]
    Xiong Z, Laird P W. COBRA: A sensitive and quantitative DNA methylation assay. Nucleic Acids Research, 1997, 25(12): 2532-2534.CrossRefGoogle Scholar
  22. [22]
    Zhou D, Qiao W, Yang L, Lu Z. Bisulfite-modified target DNA array for aberrant methylation analysis. Analytical Biochemistry, 2006, 351(1): 26-35.CrossRefGoogle Scholar
  23. [23]
    Ehrich M, Nelson M R, Stanssens P et al. Quantitative highthroughput analysis of DNA methylation patterns by basespecific cleavage and mass spectrometry. Proc. Natl. Acad. Sci. USA, 2005, 102(44): 15785-15790.CrossRefGoogle Scholar
  24. [24]
    Smith A D, Xuan Z, Zhang M Q. Using quality scores and longer reads improves accuracy of Solexa read mapping. BMC Bioinformatics, 2008, 9: 128.CrossRefGoogle Scholar
  25. [25]
    Meissner A, Mikkelsen T S, Gu H et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature, 2008, 475(7205): 766-770.Google Scholar
  26. [26]
    Ball M P, Li J B, Gao Y et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nature Biotechnology, 2009, 27(4): 361-368.CrossRefGoogle Scholar
  27. [27]
    Deng J, Shoemaker R, Xie B et al. Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming. Nature Biotechnology, 2009, 27(4): 353-360.CrossRefGoogle Scholar
  28. [28]
    Smith A D, Chung W, Hodges E, Kendall J, Hannon G, Hicks J, Xuan Z, Zhang M Q. Updates to the RMAP short-read mapping software. Bioinformatics, 2009, 25(21): 2841-2842.CrossRefGoogle Scholar
  29. [29]
    Li R, Li Y, Kristiansen K, Wang J. Soap: Short oligonucleotide alignment program. Bioinformatics, 2008, 24(5): 713-714.CrossRefGoogle Scholar
  30. [30]
    Langmead B, Trapnell C, Pop M, Salzberg S. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 2009, 10(3): R25.CrossRefGoogle Scholar
  31. [31]
    Lister R, Ecker J, Ren B. 2009. (Personal Communication)Google Scholar
  32. [32]
    Hodges E, Smith A D, Kendall J et al. High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. Genome Research, 2009, 19(9): 1593-1605.CrossRefGoogle Scholar
  33. [33]
    Eckhardt F, Lewin J, Cortese R et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet., 2006, 38(12): 1378-1385.CrossRefGoogle Scholar
  34. [34]
    Das R, Dimitrova N, Xuan Z et al. Computational prediction of methylation status in human genomic sequences. Proc. Natl. Acad. Sci. USA, 2006, 103(28): 10713-10716.CrossRefGoogle Scholar
  35. [35]
    Vilkaitis G, Suetake I, Klimasauskas S, Tajima S. Processive methylation of hemimethylated CpG sites by mouse Dnmt1 DNA methyltransferase. J. Biol. Chem., 2005, 280(1): 64-72.Google Scholar
  36. [36]
    Sebat J, Lakshmi B, Troge J et al. Large-scale copy number polymorphism in the human genome. Science, 2004, 305(5683): 525-528.CrossRefGoogle Scholar
  37. [37]
    Model F, Adorjan P, Olek A, Piepenbrock C. Feature selection for DNA methylation based cancer classification. Bioinformatics, 2001, 17(Suppl. 1): S157-S164.Google Scholar
  38. [38]
    Lister R, Ecker J R. Finding the fifth base: Genome-wide sequencing of cytosine methylation. Genome Research, 2009, 19(6): 959-968.CrossRefGoogle Scholar
  39. [39]
    Watt F, Molloy P L. Cytosine methylation prevents binding to DNA of a HeLa cell transcription factor required for optimal expression of the adenovirus major late promoter. Genes & Development, 1988, 2(9): 1136-1143.CrossRefGoogle Scholar
  40. [40]
    Bell A C, Felsenfeld G. Methylation of a CTCF-dependent boundary controls imprinted expression of the Igf 2 gene. Nature, 2000, 405(6785): 482-485.CrossRefGoogle Scholar
  41. [41]
    Lewis J D, Meehan R R, Henzel W J et al. Purification, sequence, and cellular localization of a novel chromosomal protein that binds to Methylated DNA. Cell, 1992, 69(6): 905-914.CrossRefGoogle Scholar
  42. [42]
    Klose R J, Sarraf S A, Schmiedeberg L, McDermott S M, Stancheva I, Bird A P. DNA binding selectivity of MeCP2 due to a requirement for A/T sequences adjacent to Methyl-CpG. Molecular Cell, 2005, 19(5): 667-678.CrossRefGoogle Scholar
  43. [43]
    Tompa M, Li N, Bailey T L et al. Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol., 2005, 23(1): 137-144.CrossRefGoogle Scholar
  44. [44]
    Li M, Ma B, Wang L. On the closest string and substring problems. Journal of the ACM, 2002, 49(2): 157-171.CrossRefMathSciNetGoogle Scholar
  45. [45]
    Reya T, Morrison S J, Clarke M F, Weissman I L. Stem cells, cancer, and cancer stem cells. Nature, 2001, 414(6859): 105-111.CrossRefGoogle Scholar
  46. [46]
    Riesenfeld C S, Schloss P D, Handelsman J. Metagenomics: Genomic analysis of microbial communities. Annu. Rev. Genet., 2004, 38: 525-552.CrossRefGoogle Scholar
  47. [47]
    Ford L, Fulkerson D. Flows in Networks. Princeton University Press, 1962.Google Scholar
  48. [48]
    Eriksson N, Pachter L, Mitsuya Y et al. Viral population estimation using pyrosequencing. PLoS Comput. Biol., May 2008, 4(5): e1000074.CrossRefMathSciNetGoogle Scholar
  49. [49]
    Babu M M, Lang B, Aravind L. Methods to reconstruct and compare transcriptional regulatory networks. Methods Mol. Biol., 2009, 541: 163-180.Google Scholar
  50. [50]
    Hecker M, Lambeck S, Toepfer S, van Someren E, Guthke R. Gene regulatory network inference: Data integration in dynamic models — A review. Biosystems, 2009, 96(1): 86-103.CrossRefGoogle Scholar
  51. [51]
    Bar-Joseph Z, Gerber G K, Lee T I et al. Computational discovery of gene modules and regulatory networks. Nature Biotechnology, 2003, 21(11): 1337-1342.CrossRefGoogle Scholar
  52. [52]
    Lee T I, Rinaldi N J, Robert F et al. Transcriptional regulatory networks in saccharomyces cerevisiae. Science, 2002, 298(5594): 799-804.CrossRefGoogle Scholar
  53. [53]
    Schwikowski B, Uetz P, Fields S. A network of protein-protein interactions in yeast. Nature Biotechnology, 2000, 18(12): 1257-1261.CrossRefGoogle Scholar
  54. [54]
    Beer M A, Tavazoie S. Predicting gene expression from sequence. Cell, 2004, 117(2): 185-198.CrossRefGoogle Scholar
  55. [55]
    Smith A D, Sumazin P, Xuan Z, Zhang M Q. DNA motifs in human and mouse proximal promoters predict tissue-specific expression. Proc. Natl. Acad. Sci. USA, 2006, 103(16): 6275-6280.CrossRefGoogle Scholar
  56. [56]
    Pennacchio L A, Loots G G, Nobrega M A, Ovcharenko I. Predicting tissue-specific enhancers in the human genome. Genome Research, 2007, 17(2): 201-211.CrossRefGoogle Scholar
  57. [57]
    Verona R I, Mann M R W, Bartolomei M S. Genomic imprinting: Intricacies of epigenetic regulation in clusters. Annual Review of Cell and Developmental Biology, 2003, 19(1): 237-259.CrossRefGoogle Scholar
  58. [58]
    Felsenstein J. Evolutionary trees from DNA sequences: A maximum likelihood approach. Journal of Molecular Evolution, 1981, 17(6): 368-376.CrossRefGoogle Scholar
  59. [59]
    Sankoff D. Computational complexity of inferring phylogenies by compatibility. Systematic Zoology, 1986, 35(2): 224-229.CrossRefMathSciNetGoogle Scholar
  60. [60]
    Gusfield D. Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology. Cambridge University Press, 1997.Google Scholar
  61. [61]
    Miyamoto T, Iwasaki H., Reizis B, Ye M, Graf T, Weissman I L, Akashi K. Myeloid or lymphoid promiscuity as a critical step in hematopoietic lineage commitment. Developmental Cell, 2002, 3(1): 137-147.CrossRefGoogle Scholar
  62. [62]
    Yatabe Y, Tavaré S, Shibata D. Investigating stem cells in human colon by using methylation patterns. Proc. Natl. Acad. Sci. USA, 2001, 98(19): 10839-10844.CrossRefGoogle Scholar
  63. [63]
    Kim J Y, Tavaré S, Shibata D. Counting human somatic cell replications: Methylation mirrors endometrial stem cell divisions. Proc. Natl. Acad. Sci. USA, 2005, 102(49): 17739-17744.CrossRefGoogle Scholar

Copyright information

© Springer 2010

Authors and Affiliations

  1. 1.Cold Spring Harbor LaboratoryNew YorkU.S.A.
  2. 2.Bioinformatics Division, TNLIST and MOE Key Lab of BioinformaticsTsinghua UniversityBeijingChina
  3. 3.Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesU.S.A.

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