Abstract
DNA methylation is a crucial regulatory mechanism of gene expression, affected in many human pathologies. Therefore, it is not surprising that nowadays, in the era of high-throughput methods, a lot of data sets representing DNA methylation in various conditions are available and the amount of such data keeps growing. In this chapter, we discuss those aspects of experiment planning and data analysis, which we consider the most important for reliability and reproducibility of DNA methylation studies: usage of replicates, data quality control at various stages, selection of a statistical model, and incorporation of DNA methylation into the multi-omics analysis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Jaenisch R, Bird A (2003) Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nat Genet 33(Suppl):245–254. https://doi.org/10.1038/ng1089
Messerschmidt DM, Knowles BB, Solter D (2014) DNA methylation dynamics during epigenetic reprogramming in the germline and preimplantation embryos. Genes Dev 28(8):812–828. https://doi.org/10.1101/gad.234294.113
Tomazou EM, Meissner A (2010) Epigenetic regulation of pluripotency. Adv Exp Med Biol 695:26–40. https://doi.org/10.1007/978-1-4419-7037-4_3
Horvath S (2013) DNA methylation age of human tissues and cell types. Genome Biol 14(10):R115. https://doi.org/10.1186/gb-2013-14-10-r115
Miller CA, Sweatt JD (2007) Covalent modification of DNA regulates memory formation. Neuron 53(6):857–869. https://doi.org/10.1016/j.neuron.2007.02.022
Jirtle RL, Skinner MK (2007) Environmental epigenomics and disease susceptibility. Nat Rev Genet 8(4):253–262. https://doi.org/10.1038/nrg2045
Ladd-Acosta C, Fallin MD (2016) The role of epigenetics in genetic and environmental epidemiology. Epigenomics 8(2):271–283. https://doi.org/10.2217/epi.15.102
Desai M, Jellyman JK, Ross MG (2015) Epigenomics, gestational programming and risk of metabolic syndrome. Int J Obes 39(4):633–641. https://doi.org/10.1038/ijo.2015.13
Zhong J, Agha G, Baccarelli AA (2016) The role of DNA methylation in cardiovascular risk and disease: methodological aspects, study design, and data analysis for epidemiological studies. Circ Res 118(1):119–131. https://doi.org/10.1161/CIRCRESAHA.115.305206
Wüllner U, Kaut O, deBoni L, Piston D, Schmitt I (2016) DNA methylation in Parkinson’s disease. J Neurochem 139(Suppl 1):108–120. https://doi.org/10.1111/jnc.13646
Sanchez-Mut JV, Gräff J (2015) Epigenetic alterations in Alzheimer’s disease. Front Behav Neurosci 9:347. https://doi.org/10.3389/fnbeh.2015.00347
Baylin SB, Jones PA (2016) Epigenetic determinants of cancer. Cold Spring Harb Perspect Biol 8(9). https://doi.org/10.1101/cshperspect.a019505
Klosin A, Lehner B (2016) Mechanisms, timescales and principles of trans-generational epigenetic inheritance in animals. Curr Opin Genet Dev 36:41–49. https://doi.org/10.1016/j.gde.2016.04.001
Klosin A, Casas E, Hidalgo-Carcedo C, Vavouri T, Lehner B (2017) Transgenerational transmission of environmental information in C. elegans. Science 356(6335):320–323. https://doi.org/10.1126/science.aah6412
Ito S, Shen L, Dai Q, Wu SC, Collins LB, Swenberg JA, He C, Zhang Y (2011) Tet proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine. Science 333(6047):1300–1303. https://doi.org/10.1126/science.1210597
Garcia-Manero G, Stoltz ML, Ward MR, Kantarjian H, Sharma S (2008) A pilot pharmacokinetic study of oral azacitidine. Leukemia 22(9):1680–1684. https://doi.org/10.1038/leu.2008.145
Aribi A, Borthakur G, Ravandi F, Shan J, Davisson J, Cortes J, Kantarjian H (2007) Activity of decitabine, a hypomethylating agent, in chronic myelomonocytic leukemia. Cancer 109(4):713–717. https://doi.org/10.1002/cncr.22457
Morita S, Noguchi H, Horii T, Nakabayashi K, Kimura M, Okamura K, Sakai A, Nakashima H, Hata K, Nakashima K, Hatada I (2016) Targeted DNA demethylation in vivo using dCas9–peptide repeat and scFv–TET1 catalytic domain fusions. Nat Biotechnol 34(10):1060–1065. https://doi.org/10.1038/nbt.3658
Xu X, Tao Y, Gao X, Zhang L, Li X, Zou W, Ruan K, Wang F, Xu G-L, Hu R (2016) A CRISPR-based approach for targeted DNA demethylation. Cell Discov 2:16009. https://doi.org/10.1038/celldisc.2016.9
McGregor K, Bernatsky S, Colmegna I, Hudson M, Pastinen T, Labbe A, Greenwood CMT (2016) An evaluation of methods correcting for cell-type heterogeneity in DNA methylation studies. Genome Biol 17:84. https://doi.org/10.1186/s13059-016-0935-y
Davis BM, Chao MC, Waldor MK (2013) Entering the era of bacterial epigenomics with single molecule real time DNA sequencing. Curr Opin Microbiol 16(2):192–198. https://doi.org/10.1016/j.mib.2013.01.011
Pidsley R, Zotenko E, Peters TJ, Lawrence MG, Risbridger GP, Molloy P, Van Djik S, Muhlhausler B, Stirzaker C, Clark SJ (2016) Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol 17(1):208. https://doi.org/10.1186/s13059-016-1066-1
Bock C, Tomazou EM, Brinkman AB, Müller F, Simmer F, Gu H, Jäger N, Gnirke A, Stunnenberg HG, Meissner A (2010) Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol 28(10):1106–1114. https://doi.org/10.1038/nbt.1681
Meissner A, Gnirke A, Bell GW, Ramsahoye B, Lander ES, Jaenisch R (2005) Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res 33(18):5868–5877. https://doi.org/10.1093/nar/gki901
Wachter E, Quante T, Merusi C, Arczewska A, Stewart F, Webb S, Bird A (2014) Synthetic CpG islands reveal DNA sequence determinants of chromatin structure. elife 3:e03397. https://doi.org/10.7554/eLife.03397
Krebs AR, Dessus-Babus S, Burger L, Schübeler D (2014) High-throughput engineering of a mammalian genome reveals building principles of methylation states at CG rich regions. elife 3:e04094. https://doi.org/10.7554/eLife.04094
Kapranov P, Cheng J, Dike S, Nix DA, Duttagupta R, Willingham AT, Stadler PF, Hertel J, Hackermüller J, Hofacker IL, Bell I, Cheung E, Drenkow J, Dumais E, Patel S, Helt G, Ganesh M, Ghosh S, Piccolboni A, Sementchenko V, Tammana H, Gingeras TR (2007) RNA maps reveal new RNA classes and a possible function for pervasive transcription. Science 316(5830):1484–1488. https://doi.org/10.1126/science.1138341
Hon CC, Ramilowski JA, Harshbarger J, Bertin N, Rackham OJ, Gough J, Denisenko E, Schmeier S, Poulsen TM, Severin J, Lizio M, Kawaji H, Kasukawa T, Itoh M, Burroughs AM, Noma S, Djebali S, Alam T, Medvedeva YA, Testa AC, Lipovich L, Yip CW, Abugessaisa I, Mendez M, Hasegawa A, Tang D, Lassmann T, Heutink P, Babina M, Wells CA, Kojima S, Nakamura Y, Suzuki H, Daub CO, de Hoon MJ, Arner E, Hayashizaki Y, Carninci P, Forrest AR (2017) An atlas of human long non-coding RNAs with accurate 5′ ends. Nature 543(7644):199–204. https://doi.org/10.1038/nature21374
Alam T, Medvedeva YA, Jia H, Brown JB, Lipovich L, Bajic VB (2014) Promoter analysis reveals globally differential regulation of human long non-coding RNA and protein-coding genes. PLoS One 9(10):e109443. https://doi.org/10.1371/journal.pone.0109443
Ziller MJ, Gu H, Müller F, Donaghey J, Tsai LTY, 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(7463):477–481. https://doi.org/10.1038/nature12433
Martinez-Arguelles DB, Lee S, Papadopoulos V (2014) In silico analysis identifies novel restriction enzyme combinations that expand reduced representation bisulfite sequencing CpG coverage. BMC Res Notes 7:534. https://doi.org/10.1186/1756-0500-7-534
Guo H, Zhu P, Wu X, Li X, Wen L, Tang F (2013) Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res 23(12):2126–2135. https://doi.org/10.1101/gr.161679.113
Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schönegger A, Klughammer J, Bock C (2015) Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 10(8):1386–1397. https://doi.org/10.1016/j.celrep.2015.02.001
Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, Andrews SR, Stegle O, Reik W, Kelsey G (2014) Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods 11(8):817–820. https://doi.org/10.1038/nmeth.3035
Ernst J, Kellis M (2015) Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat Biotechnol 33(4):364–376. https://doi.org/10.1038/nbt.3157
Angermueller C, Lee HJ, Reik W, Stegle O (2017) DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol 18(1):67. https://doi.org/10.1186/s13059-017-1189-z
Noordzij M, Tripepi G, Dekker FW, Zoccali C, Tanck MW, Jager KJ (2010) Sample size calculations: basic principles and common pitfalls. Nephrol Dial Transplant 25(5):1388–1393. https://doi.org/10.1093/ndt/gfp732
Ziller MJ, Hansen KD, Meissner A, Aryee MJ (2015) Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing. Nat Methods 12(3):230–232., 231 p. following 232. https://doi.org/10.1038/nmeth.3152
Libertini E, Heath SC, Hamoudi RA, Gut M, Ziller MJ, Herrero J, Czyz A, Ruotti V, Stunnenberg HG, Frontini M, Ouwehand WH, Meissner A, Gut IG, Beck S (2016) Saturation analysis for whole-genome bisulfite sequencing data. Nat Biotechnol. https://doi.org/10.1038/nbt.3524
Capra JA, Kostka D (2014) Modeling DNA methylation dynamics with approaches from phylogenetics. Bioinformatics 30(17):i408–i414. https://doi.org/10.1093/bioinformatics/btu445
Park Y, Wu H (2016) Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics 32(10):1446–1453. https://doi.org/10.1093/bioinformatics/btw026
Wright ML, Dozmorov MG, Wolen AR, Jackson-Cook C, Starkweather AR, Lyon DE, York TP (2016) Establishing an analytic pipeline for genome-wide DNA methylation. Clin Epigenetics 8:45. https://doi.org/10.1186/s13148-016-0212-7
Stockwell PA, Chatterjee A, Rodger EJ, Morison IM (2014) DMAP: differential methylation analysis package for RRBS and WGBS data. Bioinformatics 30(13):1814–1822. https://doi.org/10.1093/bioinformatics/btu126
Bock C (2012) Analysing and interpreting DNA methylation data. Nat Rev Genet 13(10):705–719. https://doi.org/10.1038/nrg3273
Park Y, Figueroa ME, Rozek LS, Sartor MA (2014) MethylSig: a whole genome DNA methylation analysis pipeline. Bioinformatics 30(17):2414–2422. https://doi.org/10.1093/bioinformatics/btu339
Dolzhenko E, Smith AD (2014) Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments. BMC Bioinformatics 15:215. https://doi.org/10.1186/1471-2105-15-215
Panchin AY, Makeev VJ, Medvedeva YA (2016) Preservation of methylated CpG dinucleotides in human CpG islands. Biol Direct 11(1):11. https://doi.org/10.1186/s13062-016-0113-x
Robinson MD, Kahraman A, Law CW, Lindsay H, Nowicka M, Weber LM, Zhou X (2014) Statistical methods for detecting differentially methylated loci and regions. Front Genet 5:324. https://doi.org/10.3389/fgene.2014.00324
Xie W, Schultz MD, Lister R, Hou Z, Rajagopal N, Ray P, Whitaker JW, Tian S, Hawkins RD, Leung D, Yang H, Wang T, Lee AY, Swanson SA, Zhang J, Zhu Y, Kim A, Nery JR, Urich MA, Kuan S, Yen C-A, Klugman S, Yu P, Suknuntha K, Propson NE, Chen H, Edsall LE, Wagner U, Li Y, Ye Z, Kulkarni A, Xuan Z, Chung W-Y, Chi NC, Antosiewicz-Bourget JE, Slukvin I, Stewart R, Zhang MQ, Wang W, Thomson JA, Ecker JR, Ren B (2013) Epigenomic analysis of multilineage differentiation of human embryonic stem cells. Cell 153(5):1134–1148. https://doi.org/10.1016/j.cell.2013.04.022
Jeong M, Sun D, Luo M, Huang Y, Challen GA, Rodriguez B, Zhang X, Chavez L, Wang H, Hannah R, Kim S-B, Yang L, Ko M, Chen R, Göttgens B, Lee J-S, Gunaratne P, Godley LA, Darlington GJ, Rao A, Li W, Goodell MA (2014) Large conserved domains of low DNA methylation maintained by Dnmt3a. Nat Genet 46(1):17–23. https://doi.org/10.1038/ng.2836
Libertini E, Heath SC, Hamoudi RA, Gut M, Ziller MJ, Czyz A, Ruotti V, Stunnenberg HG, Frontini M, Ouwehand WH, Meissner A, Gut IG, Beck S (2016) Information recovery from low coverage whole-genome bisulfite sequencing. Nat Commun 7:11306. https://doi.org/10.1038/ncomms11306
Klein HU, Hebestreit K (2016) An evaluation of methods to test predefined genomic regions for differential methylation in bisulfite sequencing data. Brief Bioinform 17(5):796–807. https://doi.org/10.1093/bib/bbv095
Medvedeva YA, Khamis AM, Kulakovskiy IV, Ba-Alawi W, MSI B, Kawaji H, Lassmann T, Harbers M, ARR F, Bajic VB, Consortium F (2014) Effects of cytosine methylation on transcription factor binding sites. BMC Genomics 15:119. https://doi.org/10.1186/1471-2164-15-119
Pardo LM, Rizzu P, Francescatto M, Vitezic M, Leday GGR, Sanchez JS, Khamis A, Takahashi H, van de Berg WDJ, Medvedeva YA, van de Wiel MA, Daub CO, Carninci P, Heutink P (2013) Regional differences in gene expression and promoter usage in aged human brains. Neurobiol Aging 34(7):1825–1836. https://doi.org/10.1016/j.neurobiolaging.2013.01.005
Zhang Y, Baheti S, Sun Z (2016) Statistical method evaluation for differentially methylated CpGs in base resolution next-generation DNA sequencing data. Brief Bioinform. https://doi.org/10.1093/bib/bbw133
Song Q, Decato B, Hong EE, Zhou M, Fang F, Qu J, Garvin T, Kessler M, Zhou J, Smith AD (2013) A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics. PLoS One 8(12):e81148. https://doi.org/10.1371/journal.pone.0081148
Medvedeva YA (2011) Algorithms for CpG islands search: new advantages and old problems. In: Mahdavi MM (ed) Bioinformatics – trends and methodologies. InTech, Rijeka. https://doi.org/10.5772/22883
Medvedeva YA, Fridman MV, Oparina NJ, Malko DB, Ermakova EO, Kulakovskiy IV, Heinzel A, Makeev VJ (2010) Intergenic, gene terminal, and intragenic CpG islands in the human genome. BMC Genomics 11:48. https://doi.org/10.1186/1471-2164-11-48
Issa J-P (2004) CpG island methylator phenotype in cancer. Nat Rev Cancer 4(12):988–993. https://doi.org/10.1038/nrc1507
Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M, Ji H, Potash JB, Sabunciyan S, Feinberg AP (2009) The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet 41(2):178–186. https://doi.org/10.1038/ng.298
Feinberg AP (2014) Epigenetic stochasticity, nuclear structure and cancer: the implications for medicine. J Intern Med 276(1):5–11. https://doi.org/10.1111/joim.12224
Hansen KD, Timp W, Bravo HC, Sabunciyan S, Langmead B, McDonald OG, Wen B, Wu H, Liu Y, Diep D, Briem E, Zhang K, Irizarry RA, Feinberg AP (2011) Increased methylation variation in epigenetic domains across cancer types. Nat Genet 43(8):768–775. https://doi.org/10.1038/ng.865
Artemov AV, Mugue NS, Rastorguev SM, Zhenilo S, Mazur AM, Tsygankova SV, Boulygina ES, Kaplun D, Nedoluzhko AV, Medvedeva YA, Prokhortchouk EB (2017) Genome-wide DNA methylation profiling reveals epigenetic adaptation of stickleback to marine and freshwater conditions. Mol Biol Evol 5:msx156
Gravina S, Dong X, Yu B, Vijg J (2016) Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome. Genome Biol 17(1):150. https://doi.org/10.1186/s13059-016-1011-3
Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for bisulfite-Seq applications. Bioinformatics 27(11):1571–1572. https://doi.org/10.1093/bioinformatics/btr167
Jin J, Lian T, Gu C, Yu K, Gao YQ, Su X-D (2016) The effects of cytosine methylation on general transcription factors. Sci Rep 6:29119. https://doi.org/10.1038/srep29119
Yin Y, Morgunova E, Jolma A, Kaasinen E, Sahu B, Khund-Sayeed S, Das PK, Kivioja T, Dave K, Zhong F, Nitta KR, Taipale M, Popov A, Ginno PA, Domcke S, Yan J, Schubeler D, Vinson C, Taipale J (2017) Impact of cytosine methylation on DNA binding specificities of human transcription factors. Science 356:6337. https://doi.org/10.1126/science.aaj2239
Jin S-G, Kadam S, Pfeifer GP (2010) Examination of the specificity of DNA methylation profiling techniques towards 5-methylcytosine and 5-hydroxymethylcytosine. Nucleic Acids Res 38(11):e125. https://doi.org/10.1093/nar/gkq223
Aran D, Hellman A (2013) DNA methylation of transcriptional enhancers and cancer predisposition. Cell 154(1):11–13. https://doi.org/10.1016/j.cell.2013.06.018
Booth MJ, Ost TWB, Beraldi D, Bell NM, Branco MR, Reik W, Balasubramanian S (2013) Oxidative bisulfite sequencing of 5-methylcytosine and 5-hydroxymethylcytosine. Nat Protoc 8(10):1841–1851. https://doi.org/10.1038/nprot.2013.115
Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, Chen Y, Zhao X, Schmidl C, Suzuki T, Ntini E, Arner E, Valen E, Li K, Schwarzfischer L, Glatz D, Raithel J, Lilje B, Rapin N, Bagger FO, Jørgensen M, Andersen PR, Bertin N, Rackham O, Burroughs AM, Baillie JK, Ishizu Y, Shimizu Y, Furuhata E, Maeda S, Negishi Y, Mungall CJ, Meehan TF, Lassmann T, Itoh M, Kawaji H, Kondo N, Kawai J, Lennartsson A, Daub CO, Heutink P, Hume DA, Jensen TH, Suzuki H, Hayashizaki Y, Müller F, Consortium F, Forrest ARR, Carninci P, Rehli M, Sandelin A (2014) An atlas of active enhancers across human cell types and tissues. Nature 507(7493):455–461. https://doi.org/10.1038/nature12787
Babenko VN, Chadaeva IV, Orlov YL (2017) Genomic landscape of CpG rich elements in human. BMC Evol Biol 17(Suppl 1):19. https://doi.org/10.1186/s12862-016-0864-0
Medvedeva YA, Lennartsson A, Ehsani R, Kulakovskiy IV, Vorontsov IE, Panahandeh P, Khimulya G, Kasukawa T, Consortium F, Drabløs F (2015) EpiFactors: a comprehensive database of human epigenetic factors and complexes. Database 2015:bav067. https://doi.org/10.1093/database/bav067
Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ, Hu TX, Krueger F, Smallwood SA, Ponting CP, Voet T, Kelsey G, Stegle O, Reik W (2016) Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods 13(3):229–232. https://doi.org/10.1038/nmeth.3728
Jeddeloh JA, Greally JM, Rando OJ (2008) Reduced-representation methylation mapping. Genome Biol 9(8):231. https://doi.org/10.1186/gb-2008-9-8-231
Acknowledgments
Y.A.M.’s work was supported by RSF grant 15-14-30002, and A.S.’s work was supported by RSF grant 14-45-00065. Y.A.M. wrote the manuscript, and A.S. wrote sections about quality control and contributed to others.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Medvedeva, Y., Shershebnev, A. (2018). Experimental Design and Bioinformatic Analysis of DNA Methylation Data. In: Vavouri, T., Peinado, M. (eds) CpG Islands. Methods in Molecular Biology, vol 1766. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7768-0_10
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7768-0_10
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7767-3
Online ISBN: 978-1-4939-7768-0
eBook Packages: Springer Protocols