Oral Biology pp 249-277 | Cite as

Tools and Strategies for Analysis of Genome-Wide and Gene-Specific DNA Methylation Patterns

  • Aniruddha Chatterjee
  • Euan J. Rodger
  • Ian M. Morison
  • Michael R. Eccles
  • Peter A. Stockwell
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1537)

Abstract

DNA methylation is a stable epigenetic mechanism that has important roles in the normal function of a cell and therefore also in disease etiology. Accurate measurements of normal and altered DNA methylation patterns are important to understand its role in regulating gene expression and cell phenotype. Remarkable progress has been made over the last decade in developing methodologies to investigate DNA methylation. The availability of next-generation sequencing has enabled the profiling of methylation marks at an unprecedented scale. Several methods that were previously used to profile locus-specific methylation have now been upgraded to a genome-wide scale using high-throughput sequencing or array platforms. However, because there are so many techniques available, researchers are faced with the challenge of assessing the potential merits or limitations of each technique and selecting the appropriate method for their analysis. In this review we discuss the strengths and weaknesses of genome-wide and gene-specific analysis tools for interrogating DNA methylation. We particularly focus on the design and analysis strategies involved. This review will provide a guideline for selecting the appropriate methods and tools for large-scale and locus-specific DNA methylation analysis.

Key words

Epigenetics DNA methylation Bisulfite sequencing RRBS WGBS 450K Next-generation DNA sequencing CpG island Differential methylation Alignment 

Notes

Acknowledgments

AC and MRE would like to thank New Zealand Institute for Cancer Research Trust, and IM would like to thank Gravida (formerly NRCGD) for their support. We would like to apologize to other research groups whose work we could not cite due to context and space limitations.

References

  1. 1.
    Carrel L, Willard HF (2005) X-inactivation profile reveals extensive variability in X-linked gene expression in females. Nature 434:400–404CrossRefPubMedGoogle Scholar
  2. 2.
    Rollins RA, Haghighi F, Edwards JR, Das R, Zhang MQ, Ju J, Bestor TH (2006) Large-scale structure of genomic methylation patterns. Genome Res 16:157–163CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Suzuki MM, Bird A (2008) DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet 9:465–476CrossRefPubMedGoogle Scholar
  4. 4.
    Igarashi J, Muroi S, Kawashima H, Wang X, Shinojima Y, Kitamura E, Oinuma T, Nemoto N, Song F, Ghosh S, Held WA, Nagase H (2008) Quantitative analysis of human tissue-specific differences in methylation. Biochem Biophys Res Commun 376:658–664CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Chatterjee A, Morison IM (2011) Monozygotic twins: genes are not the destiny? Bioinformation 7:369–370CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Chatterjee A, Eccles MR (2015) DNA methylation and epigenomics: new technologies and emerging concepts. Genome Biol 16:103CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Chatterjee A (2012) Conference scene: epigenetic regulation: from mechanism to intervention. Epigenomics 4:487–490CrossRefPubMedGoogle Scholar
  8. 8.
    Laird PW (2010) Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet 11:191–203CrossRefPubMedGoogle Scholar
  9. 9.
    Plongthongkum N, Diep DH, Zhang K (2014) Advances in the profiling of DNA modifications: cytosine methylation and beyond. Nat Rev Genet 15:647–661CrossRefPubMedGoogle Scholar
  10. 10.
    Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW, Molloy PL, Paul CL (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 89:1827–1831CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Rodger EJ, Chatterjee A, Morison IM (2014) 5-hydroxymethylcytosine: a potential therapeutic target in cancer. Epigenomics 6:503–514CrossRefPubMedGoogle Scholar
  12. 12.
    Mukhopadhyay R, Yu W, Whitehead J, Xu J, Lezcano M, Pack S, Kanduri C, Kanduri M, Ginjala V, Vostrov A, Quitschke W, Chernukhin I, Klenova E, Lobanenkov V, Ohlsson R (2004) The binding sites for the chromatin insulator protein CTCF map to DNA methylation-free domains genome-wide. Genome Res 14:1594–1602CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Miura F, Ito T (2015) Highly sensitive targeted methylome sequencing by post-bisulfite adaptor tagging. DNA Res 22:13–18CrossRefPubMedGoogle Scholar
  14. 14.
    Xi Y, Bock C, Muller F, Sun D, Meissner A, Li W (2012) RRBSMAP: a fast, accurate and user-friendly alignment tool for reduced representation bisulfite sequencing. Bioinformatics 28:430–432CrossRefPubMedGoogle Scholar
  15. 15.
    Chatterjee A, Stockwell PA, Rodger EJ, Morison IM (2012) Comparison of alignment software for genome-wide bisulphite sequence data. Nucleic Acids Res 40, e79CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Meissner A, Mikkelsen TS, Gu H, Wernig M, Hanna J, Sivachenko A, Zhang X, Bernstein BE, Nusbaum C, Jaffe DB, Gnirke A, Jaenisch R, Lander ES (2008) Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454:766–770PubMedPubMedCentralGoogle Scholar
  17. 17.
    Baranzini SE, Mudge J, van Velkinburgh JC, Khankhanian P, Khrebtukova I, Miller NA, Zhang L, Farmer AD, Bell CJ, Kim RW, May GD, Woodward JE, Caillier SJ, McElroy JP, Gomez R, Pando MJ, Clendenen LE, Ganusova EE, Schilkey FD, Ramaraj T, Khan OA, Huntley JJ, Luo S, Kwok PY, Wu TD, Schroth GP, Oksenberg JR, Hauser SL, Kingsmore SF (2010) Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis. Nature 464:1351–1356CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Bock C, Kiskinis E, Verstappen G, Gu H, Boulting G, Smith ZD, Ziller M, Croft GF, Amoroso MW, Oakley DH, Gnirke A, Eggan K, Meissner A (2011) Reference maps of human ES and iPS cell variation enable high-throughput characterization of pluripotent cell lines. Cell 144:439–452CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Gertz J, Varley KE, Reddy TE, Bowling KM, Pauli F, Parker SL, Kucera KS, Willard HF, Myers RM (2011) Analysis of DNA methylation in a three-generation family reveals widespread genetic influence on epigenetic regulation. PLoS Genet 7, e1002228CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gu H, Bock C, Mikkelsen TS, Jager N, Smith ZD, Tomazou E, Gnirke A, Lander ES, Meissner A (2010) Genome-scale DNA methylation mapping of clinical samples at single-nucleotide resolution. Nat Methods 7:133–136CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Smallwood SA, Tomizawa S, Krueger F, Ruf N, Carli N, Segonds-Pichon A, Sato S, Hata K, Andrews SR, Kelsey G (2011) Dynamic CpG island methylation landscape in oocytes and preimplantation embryos. Nat Genet 43:811–814CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Steine EJ, Ehrich M, Bell GW, Raj A, Reddy S, van Oudenaarden A, Jaenisch R, Linhart HG (2011) Genes methylated by DNA methyltransferase 3b are similar in mouse intestine and human colon cancer. J Clin Invest 121:1748–1752CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Hartung T, Zhang L, Kanwar R, Khrebtukova I, Reinhardt M, Wang C, Therneau TM, Banck MS, Schroth GP, Beutler AS (2012) Diametrically opposite methylome-transcriptome relationships in high- and low-CpG promoter genes in postmitotic neural rat tissue. Epigenetics 7:421–428CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Chatterjee A, Ozaki Y, Stockwell PA, Horsfield JA, Morison IM, Nakagawa S (2013) Mapping the zebrafish brain methylome using reduced representation bisulfite sequencing. Epigenetics 8:979–989CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Chatterjee A, Stockwell PA, Horsfield JA, Morison IM, Nakagawa S (2014) Base-resolution DNA methylation landscape of zebrafish brain and liver. Genomics Data 2:342–344CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Boyle P, Clement K, Gu H, Smith ZD, Ziller M, Fostel JL, Holmes L, Meldrim J, Kelley F, Gnirke A, Meissner A (2012) Gel-free multiplexed reduced representation bisulfite sequencing for large-scale DNA methylation profiling. Genome Biol 13:R92CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Chatterjee A, Rodger EJ, Stockwell PA, Weeks RJ, Morison IM (2012) Technical considerations for reduced representation bisulfite sequencing with multiplexed libraries. J Biomed Biotech 2012:741542CrossRefGoogle Scholar
  28. 28.
    Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM, Delano D, Zhang L, Schroth GP, Gunderson KL, Fan JB, Shen R (2011) High density DNA methylation array with single CpG site resolution. Genomics 98:288–295CrossRefPubMedGoogle Scholar
  29. 29.
    Ziller MJ, Gu H, Müller F, Donaghey J, Tsai LT, Kohlbacher O, De Jager PL, Rosen ED, Bennett DA, Bernstein BE, Gnirke A, Meissner A (2013) Charting a dynamic DNA methylation landscape of the human genome. Nature 500:477–481CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Bock C, Tomazou EM, Brinkman AB, Muller F, Simmer F, Gu H, Jager N, Gnirke A, Stunnenberg HG, Meissner A (2010) Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotech 28:1106–1114CrossRefGoogle Scholar
  31. 31.
    Matarese F, Carrillo-de Santa Pau E, Stunnenberg HG (2011) 5-Hydroxymethylcytosine: a new kid on the epigenetic block? Mol Syst Biol 7:562CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Stockwell PA, Chatterjee A, Rodger EJ, Morison IM (2014) DMAP: differential methylation analysis package for RRBS and WGBS data. Bioinformatics 30:1814–1822CrossRefPubMedGoogle Scholar
  33. 33.
    Xi Y, Li W (2009) BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10:232CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Harris EY, Ponts N, Levchuk A, Roch KL, Lonardi S (2010) BRAT: bisulfite-treated reads analysis tool. Bioinformatics 26:572–573CrossRefPubMedGoogle Scholar
  35. 35.
    Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for bisulfite-seq applications. Bioinformatics 27:1571–1572CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    Chen PY, Cokus SJ, Pellegrini M (2010) BS seeker: precise mapping for bisulfite sequencing. BMC Bioinformatics 11:203CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Pedersen B, Hsieh TF, Ibarra C, Fischer RL (2011) MethylCoder: software pipeline for bisulfite-treated sequences. Bioinformatics 27:2435–2436CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Harris EY, Ponts N, Le Roch KG, Lonardi S (2012) BRAT-BW: efficient and accurate mapping of bisulfite-treated reads. Bioinformatics 28:1795–1796CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Kreck B, Marnellos G, Richter J, Krueger F, Siebert R, Franke A (2012) B-SOLANA: an approach for the analysis of two-base encoding bisulfite sequencing data. Bioinformatics 28:428–429CrossRefPubMedGoogle Scholar
  43. 43.
    Campagna D, Telatin A, Forcato C, Vitulo N, Valle G (2013) PASS-bis: a bisulfite aligner suitable for whole methylome analysis of illumina and SOLiD reads. Bioinformatics 29:268–270CrossRefPubMedGoogle Scholar
  44. 44.
    Lim JQ, Tennakoon C, Li G, Wong E, Ruan Y, Wei CL, Sung WK (2012) BatMeth: improved mapper for bisulfite sequencing reads on DNA methylation. Genome Biol 13:R82CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Morris TJ, Beck S (2015) Analysis pipelines and packages for Infinium HumanMethylation450 BeadChip (450 k) data. Methods 72:3–8CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Dedeurwaerder S, Defrance M, Bizet M, Calonne E, Bontempi G, Fuks F (2014) A comprehensive overview of Infinium HumanMethylation450 data processing. Brief Bioinform 15:929–941CrossRefPubMedGoogle Scholar
  47. 47.
    Chen YA, Lemire M, Choufani S, Butcher DT, Grafodatskaya D, Zanke BW, Gallinger S, Hudson TJ, Weksberg R (2013) Discovery of cross-reactive probes and polymorphic CpGs in the Illumina Infinium HumanMethylation450 microarray. Epigenetics 2013(8):203–209CrossRefGoogle Scholar
  48. 48.
    Price EM, Cotton AM, Lam LL, Farre P, Emberly E, Brown CJ, Robinson WP, Kobor MS (2013) Additional annotation enhances potential for biologically-relevant analysis of the Illumina Infinium HumanMethylation450 BeadChip array. Epigenetics Chromatin 6:4CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Zhang X, Mu W, Zhang W (2012) On the analysis of the illumina 450 k array data: probes ambiguously mapped to the human genome. Front Genet 3:73PubMedPubMedCentralGoogle Scholar
  50. 50.
    Xu Z, Niu L, Li L, Taylor JA (2015) ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res 44, e20CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Marabita F, Almgren M, Lindholm ME, Ruhrmann S, Fagerstrom-Billai F, Jagodic M, Sundberg CJ, Ekstrom TJ, Teschendorff AE, Tegner J, Gomez-Cabrero D (2013) An evaluation of analysis pipelines for DNA methylation profiling using the Illumina HumanMethylation450 BeadChip platform. Epigenetics 8:333–346CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Triche TJ Jr, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD (2013) Low-level processing of illumina infinium DNA methylation BeadArrays. Nucleic Acids Res 41, e90CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Davis S, Du P, Bilke S, Triche T Jr, Bootwalla M (2015) Methylumi: handle illumina methylation data. R Package Version 2160 2015Google Scholar
  54. 54.
    Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, Irizarry RA (2014) Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics 30:1363–1369CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J, Gomez-Cabrero D, Beck S (2013) A beta-mixture quantile normalization method for correcting probe design bias in illumina infinium 450 k DNA methylation data. Bioinformatics 29:189–196CrossRefPubMedGoogle Scholar
  56. 56.
    Pidsley R, Y Wong CC, Volta M, Lunnon K, Mill J, Schalkwyk LC (2013) A data-driven approach to preprocessing illumina 450K methylation array data. BMC Genomics 14:293CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Wang D, Yan L, Hu Q, Sucheston LE, Higgins MJ, Ambrosone CB, Johnson CS, Smiraglia DJ, Liu S (2012) IMA: an R package for high-throughput analysis of Illumina’s 450 K infinium methylation data. Bioinformatics 28:729–730CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, Hou L, Lin SM (2010) Comparison of beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11:587CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Zhuang J, Widschwendter M, Teschendorff AE (2012) A comparison of feature selection and classification methods in DNA methylation studies using the illumina infinium platform. BMC Bioinformatics 13:59CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Zou J, Lippert C, Heckerman D, Aryee M, Listgarten J (2014) Epigenome-wide association studies without the need for cell-type composition. Nat Methods 11:309–311CrossRefPubMedGoogle Scholar
  61. 61.
    Houseman EA, Molitor J, Marsit CJ (2014) Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics 30:1431–1439CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14:R115CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, Bibikova M, Fan JB, Gao Y, Deconde R, Chen M, Rajapakse I, Friend S, Ideker T, Zhang K (2013) Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell 49:359–367CrossRefPubMedGoogle Scholar
  64. 64.
    Stewart SK, Morris TJ, Guilhamon P, Bulstrode H, Bachman M, Balasubramanian S, Beck S (2015) oxBS-450 K: a method for analysing hydroxymethylation using 450 K BeadChips. Methods 72:9–15CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Akalin A, Kormaksson M, Li S, Garrett-Bakelman FE, Figueroa ME, Melnick A, Mason CE (2012) methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol 13:R87CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    Wang HQ, Tuominen LK, Tsai CJ (2011) SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures. Bioinformatics 27:225–231CrossRefPubMedGoogle Scholar
  67. 67.
    Ehrlich M, Lacey M (2013) DNA methylation and differentiation: silencing, upregulation and modulation of gene expression. Epigenomics 5:553–568CrossRefPubMedGoogle Scholar
  68. 68.
    Bock C, Beerman I, Lien WH, Smith ZD, Gu H, Boyle P, Gnirke A, Fuchs E, Rossi DJ, Meissner A (2012) DNA methylation dynamics during in vivo differentiation of blood and skin stem cells. Mol Cell 47:633–647CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Li Y, Zhu J, Tian G, Li N, Li Q, Ye M, Zheng H, Yu J, Wu H, Sun J, Zhang H, Chen Q, Luo R, Chen M, He Y, Jin X, Zhang Q, Yu C, Zhou G, Sun J, Huang Y, Zheng H, Cao H, Zhou X, Guo S, Hu X, Li X, Kristiansen K, Bolund L, Xu J, Wang W, Yang H, Wang J, Li R, Beck S, Wang J, Zhang X (2010) The DNA methylome of human peripheral blood mononuclear cells. PLoS Biol 8, e1000533CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Hansen KD, Langmead B, Irizarry RA (2012) BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol 13:R83CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Hebestreit K, Dugas M, Klein HU (2013) Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 29:1647–1653CrossRefPubMedGoogle Scholar
  72. 72.
    Chatterjee A, Stockwell PA, Rodger EJ, Duncan EJ, Parry MF, Weeks RJ, Morison IM (2015) Genome-wide DNA methylation map of human neutrophils reveals widespread inter-individual epigenetic variation. Sci Rep 5:17328CrossRefPubMedPubMedCentralGoogle Scholar
  73. 73.
    Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29:24–26CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Halachev K, Bast H, Albrecht F, Lengauer T, Bock C (2012) EpiExplorer: live exploration and global analysis of large epigenomic datasets. Genome Biol 13:R96CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    Li S, Garrett-Bakelman F, Perl AE, Luger SM, Zhang C, To BL, Lewis ID, Brown AL, D’Andrea RJ, Ross ME et al (2014) Dynamic evolution of clonal epialleles revealed by methclone. Genome Biol 15:472CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Clark SJ, Harrison J, Paul CL, Frommer M (1994) High sensitivity mapping of methylated cytosines. Nucleic Acids Res 22:2990–2997CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    Tost J, Gut IG (2007) Analysis of gene-specific DNA methylation patterns by pyrosequencing technology. Methods Mol Biol 373:89–102PubMedGoogle Scholar
  78. 78.
    Masser DR, Berg AS, Freeman WM (2013) Focused, high accuracy 5-methylcytosine quantitation with base resolution by benchtop next-generation sequencing. Epigenetics Chromatin 6:33CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    Diep D, Plongthongkum N, Gore A, Fung HL, Shoemaker R, Zhang K (2012) Library-free methylation sequencing with bisulfite padlock probes. Nat Methods 9:270–272CrossRefPubMedPubMedCentralGoogle Scholar
  80. 80.
    Komori HK, LaMere SA, Torkamani A, Hart GT, Kotsopoulos S, Warner J, Samuels ML, Olson J, Head SR, Ordoukhanian P, Lee PL, Link DR, Salomon DR (2011) Application of microdroplet PCR for large-scale targeted bisulfite sequencing. Genome Res 21:1738–1745CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Herman JG, Graff JR, Myohanen S, Nelkin BD, Baylin SB (1996) Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc Natl Acad Sci U S A 93:9821–9826CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Kristensen LS, Mikeska T, Krypuy M, Dobrovic A (2008) Sensitive melting analysis after real time-methylation specific PCR (SMART-MSP): high-throughput and probe-free quantitative DNA methylation detection. Nucleic Acids Res 36, e42CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Thomassin H, Kress C, Grange T (2004) MethylQuant: a sensitive method for quantifying methylation of specific cytosines within the genome. Nucleic Acids Res 32, e168CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Eads CA, Danenberg KD, Kawakami K, Saltz LB, Blake C, Shibata D, Danenberg PV, Laird PW (2000) MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res 28, E32CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Hernandez HG, Tse MY, Pang SC, Arboleda H, Forero DA (2013) Optimizing methodologies for PCR-based DNA methylation analysis. Biotechniques 55:181–197CrossRefPubMedGoogle Scholar
  86. 86.
    Coolen MW, Statham AL, Gardiner-Garden M, Clark SJ (2007) Genomic profiling of CpG methylation and allelic specificity using quantitative high-throughput mass spectrometry: critical evaluation and improvements. Nucleic Acids Res 35, e119CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Christensen BC, Kelsey KT, Zheng S, Houseman EA, Marsit CJ, Wrensch MR, Wiemels JL, Nelson HH, Karagas MR, Kushi LH, Kwan ML, Wiencke JK (2010) Breast cancer DNA methylation profiles are associated with tumor size and alcohol and folate intake. PLoS Genet 6, e1001043CrossRefPubMedPubMedCentralGoogle Scholar
  88. 88.
    Breitling LP, Yang R, Korn B, Burwinkel B, Brenner H (2011) Tobacco-smoking-related differential DNA methylation: 27 K discovery and replication. Am J Hum Genet 88:450–457CrossRefPubMedPubMedCentralGoogle Scholar
  89. 89.
    Figueroa ME, Lugthart S, Li Y, Erpelinck-Verschueren C, Deng X, Christos PJ, Schifano E, Booth J, van Putten W, Skrabanek L, Campagne F, Mazumdar M, Greally JM, Valk PJ, Löwenberg B, Delwel R, Melnick A (2010) DNA methylation signatures identify biologically distinct subtypes in acute myeloid leukemia. Cancer Cell 17:13–27CrossRefPubMedPubMedCentralGoogle Scholar
  90. 90.
    Farthing CR, Ficz G, Ng RK, Chan CF, Andrews S, Dean W, Hemberger M, Reik W (2008) Global mapping of DNA methylation in mouse promoters reveals epigenetic reprogramming of pluripotency genes. PLoS Genet 4, e1000116CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Liang P, Song F, Ghosh S, Morien E, Qin M, Mahmood S, Fujiwara K, Igarashi J, Nagase H, Held WA (2011) Genome-wide survey reveals dynamic widespread tissue-specific changes in DNA methylation during development. BMC Genomics 12:231CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Fuso A, Ferraguti G, Scarpa S, Ferrer I, Lucarelli M (2015) Disclosing bias in bisulfite assay: MethPrimers underestimate high DNA methylation. PLoS One 10, e0118318CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Park Y, Figueroa ME, Rozek LS, Sartor MA (2014) MethylSig: a whole genome DNA methylation analysis pipeline. Bioinformatics 30:2414–2422CrossRefPubMedPubMedCentralGoogle Scholar
  94. 94.
    Dolzhenko E, Smith AD (2014) Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinformatics 15:215CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Assenov Y, Muller F, Lutsik P, Walter J, Lengauer T, Bock C (2014) Comprehensive analysis of DNA methylation data with RnBeads. Nat Methods 11:1138–1140CrossRefPubMedPubMedCentralGoogle Scholar
  96. 96.
    Morris TJ, Butcher LM, Feber A, Teschendorff AE, Chakravarthy AR, Wojdacz TK, Beck S (2014) ChAMP: 450 k chip analysis methylation pipeline. Bioinformatics 30:428–430CrossRefPubMedGoogle Scholar
  97. 97.
    Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, V Lord R, Clark SJ, Molloy PL (2015) De novo identification of differentially methylated regions in the human genome. Epigenetics Chromatin 8:6PubMedPubMedCentralGoogle Scholar
  98. 98.
    Phipson B, Maksimovic J, Oshlack A (2015) missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics 32:286–288PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Aniruddha Chatterjee
    • 1
    • 2
  • Euan J. Rodger
    • 1
  • Ian M. Morison
    • 1
    • 2
  • Michael R. Eccles
    • 1
    • 4
  • Peter A. Stockwell
    • 3
  1. 1.Department of Pathology, Dunedin School of MedicineUniversity of OtagoDunedinNew Zealand
  2. 2.Gravida: National Centre for Growth and DevelopmentUniversity of AucklandGraftonNew Zealand
  3. 3.Department of BiochemistryUniversity of OtagoDunedinNew Zealand
  4. 4.Maurice Wilkins Centre forMolecular BiodiscoveryAucklandNew Zealand

Personalised recommendations