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Charting a dynamic DNA methylation landscape of the human genome
Open accessAbstract
DNA methylation is a defining feature of mammalian cellular identity and is essential for normal development. Most cell types, except germ cells and pre-implantation embryos, display relatively stable DNA methylation patterns, with 70-80% of all CpGs being methylated. Despite recent advances, we still have a limited understanding of when, where and how many CpGs participate in genomic regulation. Here we report the in-depth analysis of 42 whole-genome bisulphite sequencing data sets across 30 diverse human cell and tissue types. We observe dynamic regulation for only 21.8% of autosomal CpGs within a normal developmental context, most of which are distal to transcription start sites. These dynamic CpGs co-localize with gene regulatory elements, particularly enhancers and transcription-factor-binding sites, which allow identification of key lineage-specific regulators. In addition, differentially methylated regions (DMRs) often contain single nucleotide polymorphisms associated with cell-type-related diseases as determined by genome-wide association studies. The results also highlight the general inefficiency of whole-genome bisulphite sequencing, as 70-80% of the sequencing reads across these data sets provided little or no relevant information about CpG methylation. To demonstrate further the utility of our DMR set, we use it to classify unknown samples and identify representative signature regions that recapitulate major DNA methylation dynamics. In summary, although in theory every CpG can change its methylation state, our results suggest that only a fraction does so as part of coordinated regulatory programs. Therefore, our selected DMRs can serve as a starting point to guide new, more effective reduced representation approaches to capture the most informative fraction of CpGs, as well as further pinpoint putative regulatory elements.
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“These dynamically methylated CpGs cluster into over 1 million tissue specific differentially methylated regions (DMRs).”
From Dynamic DNA methylation: In the right place at the right time (2018) · cites this paper as [5]
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“direct comparison of whole genome methylome profiles derived from numerous cell types has revealed that under normal physiological conditions 15%-21% of CpGs vary in methylation status between human tissues (4, 5).”
From Dynamic DNA methylation: In the right place at the right time (2018) · cites this paper as [5]
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Cites
DNA methylation patterns and epigenetic memory
Bird, A. (2002) · Genes & Development · doi:10.1101/gad.947102 · Cited by 7,124
Human DNA methylomes at base resolution show widespread epigenomic differences
Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., et al. (2009) · Nature · doi:10.1038/nature08514 · Cited by 4,673
Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013
Systematic Localization of Common Disease-Associated Variation in Regulatory DNA
Maurano, M. T., Humbert, R., Rynes, E., Thurman, R. E., Haugen, E., Wang, H., Reynolds, A. P., Sandstrom, R., Qu, H., Brody, J., et al. (2012) · Science · doi:10.1126/science.1222794 · Cited by 3,716
DNA methylation: roles in mammalian development
Smith, Z. D., & Meissner, A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3354 · Cited by 3,045
Self-citationThe accessible chromatin landscape of the human genome
Thurman, R. E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M. T., Haugen, E., Sheffield, N. C., Stergachis, A. B., Wang, H., Vernot, B., et al. (2012) · Nature · doi:10.1038/nature11232 · Cited by 2,679
Mapping short DNA sequencing reads and calling variants using mapping quality scores
Li, H., Ruan, J., & Durbin, R. (2008) · Genome Research · doi:10.1101/gr.078212.108 · Cited by 2,638
Genome-scale DNA methylation maps of pluripotent and differentiated cells
Meissner, A., Mikkelsen, T. S., Gu, H., Wernig, M., Hanna, J., Sivachenko, A., Zhang, X., Bernstein, B. E., Nusbaum, C., Jaffe, D. B., et al. (2008) · Nature · doi:10.1038/nature07107 · Cited by 2,536
Self-citationIrizarry, R. A., Ladd-Acosta, C., Wen, B., Wu, Z., Montano, C., Onyango, P., Cui, H., Gabo, K., Rongione, M., Webster, M., et al. (2009) · Nature Genetics · doi:10.1038/ng.298 · Cited by 2,177
The DNA methyltransferases of mammals
Bestor, T. H. (2000) · Human Molecular Genetics · doi:10.1093/hmg/9.16.2395 · Cited by 2,078
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Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
“…uthor M anuscript A uthor M anuscript with the loss of DNA methylation at these regions, we determined binding sites of a compendium of transcription factors profiled in distinct cell lines and types (see Ziller, 2013 #45 for details) that overlapped with each set of hypomethylated DMRs.”
Self-citationNext-generation characterization of the Cancer Cell Line Encyclopedia
Ghandi, M., Huang, F. W., Jané-Valbuena, J., Kryukov, G. V., Lo, C. C., McDonald, E. R., Barretina, J., Gelfand, E. T., Bielski, C. M., Li, H., et al. (2019) · Nature · doi:10.1038/s41586-019-1186-3 · Cited by 3,394
The diverse roles of DNA methylation in mammalian development and disease
Greenberg, M., & Bourc’his, D. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0159-6 · Cited by 2,272
Function and information content of DNA methylation
Schübeler, D. (2015) · Nature · doi:10.1038/nature14192 · Cited by 2,060
Pidsley, R., Zotenko, E., Peters, T. J., Lawrence, M. G., Risbridger, G. P., Molloy, P., Van Djik, S., Muhlhausler, B., Stirzaker, C., & Clark, S. J. (2016) · Genome biology · doi:10.1186/s13059-016-1066-1 · Cited by 1,480
Religious Orders Study and Rush Memory and Aging Project
Bennett, D. A., Buchman, A. S., Boyle, P. A., Barnes, L. L., Wilson, R. S., & Schneider, J. A. (2018) · Journal of Alzheimer's Disease · doi:10.3233/jad-179939 · Cited by 1,260
Self-citationTranscriptional Addiction in Cancer
Bradner, J. E., Hnisz, D., & Young, R. A. (2017) · Cell · doi:10.1016/j.cell.2016.12.013 · Cited by 1,221
Regulatory T Cells and Human Disease
Sakaguchi, S., Mikami, N., Wing, J. B., Tanaka, A., Ichiyama, K., & Ohkura, N. (2020) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042718-041717 · Cited by 1,163
Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity
Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125
Reversing DNA Methylation: Mechanisms, Genomics, and Biological Functions
Wu, H., & Zhang, Y. (2014) · Cell · doi:10.1016/j.cell.2013.12.019 · Cited by 1,065
Role of TET enzymes in DNA methylation, development, and cancer
Rasmussen, K. D., & Helin, K. (2016) · Genes & Development · doi:10.1101/gad.276568.115 · Cited by 1,040
“Thus, in contrast to a relatively static DNAmethylation environment, the gain and loss of DNA methylation at regulatory regions are prominent features of normal development (Hon et al. 2013; Ziller et al. 2013).”
BackgroundDNA methylation aging clocks: challenges and recommendations
Bell, C. G., Lowe, R., Adams, P. D., Baccarelli, A. A., Beck, S., Bell, J. T., Christensen, B. C., Gladyshev, V. N., Heijmans, B. T., Horvath, S., et al. (2019) · Genome Biology · doi:10.1186/s13059-019-1824-y · Cited by 1,025
Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., Eaton, M. L., Keenan, B. T., Ernst, J., McCabe, C., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3786 · Cited by 956
“Subsequently, CpG methylation calls were made using custom software 57 , excluding duplicate, low quality reads as well as reads with more than 10% mismatches.”
Self-citationDNA methylation and healthy human aging
Jones, M. J., Goodman, S. J., & Kobor, M. S. (2015) · Aging Cell · doi:10.1111/acel.12349 · Cited by 973
Epigenetic Signatures of Cigarette Smoking
Joehanes, R., Just, A. C., Marioni, R. E., Pilling, L. C., Reynolds, L. M., Mandaviya, P. R., Guan, W., Xu, T., Elks, C. E., Aslibekyan, S. W., et al. (2016) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001506 · Cited by 934
DNA methylation in newborns and maternal smoking in pregnancy: genome-wide consortium meta-analysis
Joubert, B. R., Felix, J. F., Yousefi, P., Bakulski, K. M., Just, A. C., Breton, C., Reese, S. E., Markunas, C. A., Richmond, R. C., Xu, C.-J., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.019 · Cited by 886
DNA methylation: a historical perspective
Mattei, A. L., Bailly, N., & Meissner, A. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.010 · Cited by 920
Self-citationTurecki, G., & Meaney, M. J. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.11.022 · Cited by 796
Interpreting the language of histone and DNA modifications
Rothbart, S. B., & Strahl, B. D. (2014) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2014.03.001 · Cited by 792
Human body epigenome maps reveal noncanonical DNA methylation variation
Schultz, M. D., He, Y., Whitaker, J. W., Hariharan, M., Mukamel, E. A., Leung, D., Rajagopal, N., Nery, J. R., Urich, M. A., Chen, H., et al. (2015) · Nature · doi:10.1038/nature14465 · Cited by 690
Sun, D., Luo, M., Jeong, M., Rodriguez, B., Xia, Z., Hannah, R., Wang, H., Le, T., Faull, K. F., Chen, R., et al. (2014) · Cell Stem Cell · doi:10.1016/j.stem.2014.03.002 · Cited by 686
Self-citationDynamic DNA methylation: In the right place at the right time
Luo, C., Hajkova, P., & Ecker, J. R. (2018) · Science · doi:10.1126/science.aat6806 · Cited by 668
A DNA methylation atlas of normal human cell types
Loyfer, N., Magenheim, J., Peretz, A., Cann, G., Bredno, J., Klochendler, A., Fox-Fisher, I., Shabi-Porat, S., Hecht, M., Pelet, T., et al. (2023) · Nature · doi:10.1038/s41586-022-05580-6 · Cited by 656
Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., et al. (2016) · Cell Research · doi:10.1038/cr.2016.23 · Cited by 647
Principles of DNA methylation and their implications for biology and medicine
Dor, Y., & Cedar, H. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31268-6 · Cited by 640
DNA methylation signatures link prenatal famine exposure to growth and metabolism
Tobi, E. W., Goeman, J. J., Monajemi, R., Gu, H., Putter, H., Zhang, Y., Slieker, R. C., Stok, A. P., Thijssen, P. E., Müller, F., et al. (2014) · Nature Communications · doi:10.1038/ncomms6592 · Cited by 630
Self-citationComprehensive analysis of DNA methylation data with RnBeads
Assenov, Y., Müller, F., Lutsik, P., Walter, J., Lengauer, T., & Bock, C. (2014) · Nature Methods · doi:10.1038/nmeth.3115 · Cited by 602
Self-citationFeng, H., Conneely, K. N., & Wu, H. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku154 · Cited by 596
DNA methylation dynamics of the human preimplantation embryo
Smith, Z. D., Chan, M. M., Humm, K. C., Karnik, R., Mekhoubad, S., Regev, A., Eggan, K. C., & Meissner, A. (2014) · Nature · doi:10.1038/nature13581 · Cited by 600
Self-citationKipps, T. J., Stevenson, F. K., Wu, C. J., Croce, C. M., Packham, G., Wierda, W. G., O'Brien, S. M., Gribben, J. G., & Rai, K. (2017) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2016.96 · Cited by 590
The alternative role of DNA methylation in splicing regulation
Lev-Maor, G., Yearim, A., & Ast, G. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.03.002 · Cited by 604
Rivera, C. M., & Ren, B. (2013) · Cell · doi:10.1016/j.cell.2013.09.011 · Cited by 550
DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning
Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536
Programming and Inheritance of Parental DNA Methylomes in Mammals
Wang, L., Zhang, J., Duan, J., Gao, X., Zhu, W. Z., Lu, X., Yang, L., Zhang, J., Li, G., Ci, W., et al. (2014) · Cell · doi:10.1016/j.cell.2014.04.017 · Cited by 556
Guo, S., Diep, D. H., Plongthongkum, N., Fung, H.-L., Zhang, K., & Zhang, K. (2017) · Nature Genetics · doi:10.1038/ng.3805 · Cited by 528
Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex
Jaffe, A. E., Gao, Y., Deep-Soboslay, A., Tao, R., Hyde, T. M., Weinberger, D. R., & Kleinman, J. E. (2016) · Nature Neuroscience · doi:10.1038/nn.4181 · Cited by 511
DNMT3A in haematological malignancies
Yang, L., Rau, R. E., & Goodell, M. A. (2015) · Nature reviews. Cancer · doi:10.1038/nrc3895 · Cited by 525
Competition between DNA methylation and transcription factors determines binding of NRF1
Domcke, S., Bardet, A. F., Adrian Ginno, P., Hartl, D., Burger, L., & Schübeler, D. (2015) · Nature · doi:10.1038/nature16462 · Cited by 508
Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
Min, J. L., Hemani, G., Hannon, E., Dekkers, K. F., Castillo-Fernandez, J., Luijk, R., Carnero-Montoro, E., Lawson, D. J., Burrows, K., Suderman, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00923-x · Cited by 506
Universal DNA methylation age across mammalian tissues
Lu, A. T.-H., Fei, Z., Haghani, A., Robeck, T. R., Zoller, J. A., Li, C. Z., Lowe, R., Yan, Q. J., Zhang, J., Vu, H., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00462-6 · Cited by 510
Targeted disruption of DNMT1, DNMT3A and DNMT3B in human embryonic stem cells
Liao, J., Karnik, R., Gu, H., Ziller, M. J., Clement, K., Tsankov, A. M., Akopian, V., Gifford, C. A., Donaghey, J., Galonska, C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3258 · Cited by 497
Self-citationSun, Y. V., & Hu, Y.-J. (2016) · Advances in Genetics · doi:10.1016/bs.adgen.2015.11.004 · Cited by 459
Russler-Germain, D. A., Spencer, D. H., Young, M. A., Lamprecht, T. L., Miller, C. A., Fulton, R., Meyer, M. R., Erdmann-Gilmore, P., Townsend, R. R., Wilson, R. K., et al. (2014) · Cancer Cell · doi:10.1016/j.ccr.2014.02.010 · Cited by 452
Editing the epigenome: technologies for programmable transcription and epigenetic modulation
Thakore, P. I., Black, J. B., Hilton, I. B., & Gersbach, C. A. (2016) · Nature Methods · doi:10.1038/nmeth.3733 · Cited by 451
Anastasiadi, D., Esteve‐Codina, A., & Piferrer, F. (2018) · Epigenetics & Chromatin · doi:10.1186/s13072-018-0205-1 · Cited by 461
Mitochondrial dysfunction in type 2 diabetes mellitus: an organ-based analysis
Pinti, M. V., Fink, G. K., Hathaway, Q. A., Durr, A. J., Kunovac, A., & Hollander, J. M. (2019) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00314.2018 · Cited by 447
Epigenetics across the human lifespan
Kanherkar, R. R., Bhatia-Dey, N., & Csoka, A. B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00049 · Cited by 407
Genetic and environmental influences interact with age and sex in shaping the human methylome
van Dongen, J., Nivard, M. G., Willemsen, G. A. H. M., Hottenga, J., Helmer, Q., Dolan, C. V., Ehli, E. A., Davies, G. E., van Iterson, M., Breeze, C. E., et al. (2016) · Nature Communications · doi:10.1038/ncomms11115 · Cited by 411
Landau, D. A., Clement, K., Ziller, M. J., Boyle, P., Fan, J., Gu, H., Stevenson, K. E., Sougnez, C. L., Wang, L., Li, S., et al. (2014) · Cancer Cell · doi:10.1016/j.ccell.2014.10.012 · Cited by 401
Self-citationReprogramming the Methylome: Erasing Memory and Creating Diversity
Lee, H. J., Hore, T. A., & Reik, W. (2014) · Cell stem cell · doi:10.1016/j.stem.2014.05.008 · Cited by 397
The epigenetics of aging and neurodegeneration
Lardenoije, R., Iatrou, A., Kenis, G., Kompotis, K., Steinbusch, H. W. M., Mastroeni, D., Coleman, P., Lemere, C. A., Hof, P. R., van den Hove, D. L. A., et al. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.05.002 · Cited by 381
Rapid and Pervasive Changes in Genome-wide Enhancer Usage during Mammalian Development
Nord, A. S., Blow, M. J., Attanasio, C., Akiyama, J. A., Holt, A., Hosseini, R., Phouanenavong, S., Plajzer-Frick, I., Shoukry, M., Afzal, V., et al. (2013) · Cell · doi:10.1016/j.cell.2013.11.033 · Cited by 375
Cerebral Organoids Recapitulate Epigenomic Signatures of the Human Fetal Brain
Luo, C., Lancaster, M. A., Castanon, R., Nery, J. R., Knoblich, J. A., & Ecker, J. R. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.12.001 · Cited by 370
RnBeads 2.0: comprehensive analysis of DNA methylation data
Müller, F., Scherer, M., Assenov, Y., Lutsik, P., Walter, J., Lengauer, T., & Bock, C. (2019) · Genome biology · doi:10.1186/s13059-019-1664-9 · Cited by 383
Self-citationvan Dijk, S. J., Molloy, P. L., Varinli, H., Morrison, J. L., Mühlhäusler, B. S., EpiSCOPE, M. O., Buckley, M. J., Clark, S. J., McMillen, I. C., Noakes, M., et al. (2014) · International Journal of Obesity · doi:10.1038/ijo.2014.34 · Cited by 364
Candidate Gene–Environment Interaction Research
Dick, D. M., Arpana Agrawal, A., Keller, M. C., Adkins, A. E., Alıev, F., Monroe, S. M., Hewitt, J. K., Kendler, K. S., & Sher, K. J. (2015) · Perspectives on Psychological Science · doi:10.1177/1745691614556682 · Cited by 383
Smith, A. K., Kilaru, V., Klengel, T., Mercer, K. B., Bradley, B., Conneely, K. N., Ressler, K. J., & Binder, E. B. (2015) · American Journal of Medical Genetics Part B: Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32278 · Cited by 355
Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues
Ernst, J., & Kellis, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3157 · Cited by 357
Transcription factor binding dynamics during human ES cell differentiation
Tsankov, A. M., Gu, H., Akopian, V., Ziller, M. J., Donaghey, J., Amit, I., Gnirke, A., & Meissner, A. (2015) · Nature · doi:10.1038/nature14233 · Cited by 358
Self-citationSensitivity of transcription factors to DNA methylation
Héberlé, É., & Bardet, A. F. (2019) · Essays in Biochemistry · doi:10.1042/ebc20190033 · Cited by 347
Tak, Y. G., & Farnham, P. (2015) · Epigenetics & Chromatin · doi:10.1186/s13072-015-0050-4 · Cited by 353
Individualized Medicine from Prewomb to Tomb
Topol, E. J. (2014) · Cell · doi:10.1016/j.cell.2014.02.012 · Cited by 358
Methylation QTLs in the developing brain and their enrichment in schizophrenia risk loci
Hannon, E. J., Spiers, H., Viana, J. F., Pidsley, R., Burrage, J., Murphy, T. M., Troakes, C., Turecki, G., O’Donovan, M. C., Schalkwyk, L. C., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4182 · Cited by 350
Hon, G. C., Song, C., Du, T., Jin, F., Selvaraj, S., Lee, A. Y., Yen, C., Ye, Z., Mao, S.-Q., Wang, B.-A., et al. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.08.026 · Cited by 349
Whole-genome fingerprint of the DNA methylome during human B cell differentiation
Kulis, M., Merkel, A., Heath, S. C., Queirós, A. C., Schuyler, R. P., Castellano, G., Beekman, R., Raineri, E., Esteve‐Codina, A., Clot, G., et al. (2015) · Nature Genetics · doi:10.1038/ng.3291 · Cited by 336
Kieffer-Kwon, K.-R., Tang, Z., Mathé, E. A., Qian, J., Sung, M., Li, G., Resch, W., Baek, S., Pruett, N. D., Grøntved, L., et al. (2013) · Cell · doi:10.1016/j.cell.2013.11.039 · Cited by 337
Dynamic DNA methylation orchestrates cardiomyocyte development, maturation and disease
Gilsbach, R., Preißl, S., Grüning, B. A., Schnick, T., Burger, L., Beneš, V., Würch, A., Bönisch, U., Günther, S., Backofen, R., et al. (2014) · Nature Communications · doi:10.1038/ncomms6288 · Cited by 328
Epigenetic inheritance and the missing heritability
Trerotola, M., Relli, V., Simeone, P., & Alberti, S. (2015) · Human Genomics · doi:10.1186/s40246-015-0041-3 · Cited by 318
“Approximately 60–80 % of the 28 million CpG dinucleotides in the human genome are methylated [18, 19].”
BackgroundDNA Methylation Biomarkers in Aging and Age-Related Diseases
Salameh, Y., Bejaoui, Y., & Hajj, N. E. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00171 · Cited by 322
The epigenetic basis of cellular heterogeneity
Carter, B. C., & Zhao, K. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-00300-0 · Cited by 324
Epigenetics, plasticity, and evolution: How do we link epigenetic change to phenotype?
Duncan, E. J., Gluckman, P. D., & Dearden, P. K. (2014) · Journal of Experimental Zoology Part B Molecular and Developmental Evolution · doi:10.1002/jez.b.22571 · Cited by 329
Coverage recommendations for methylation analysis by whole-genome bisulfite sequencing
Ziller, M. J., Hansen, K. D., Meissner, A., & Aryee, M. J. (2014) · Nature Methods · doi:10.1038/nmeth.3152 · Cited by 314
Self-citationOakes, C. C., Seifert, M., Assenov, Y., Gu, L., Przekopowitz, M., Ruppert, A. S., Wang, Q., Imbusch, C. D., Serva, A., Koser, S. D., et al. (2016) · Nature Genetics · doi:10.1038/ng.3488 · Cited by 309
Thompson, P. J., Macfarlan, T. S., & Lorincz, M. C. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.03.029 · Cited by 317
Statistical and integrative system-level analysis of DNA methylation data
Teschendorff, A. E., & Relton, C. L. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.86 · Cited by 297
Landscape and Regulation of m6A and m6Am Methylome across Human and Mouse Tissues
Liu, J., Li, K., Cai, J., Zhang, M., Zhang, X., Xiong, X., Meng, H., Xu, X., Huang, Z., Peng, J., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.09.032 · Cited by 291
Methylomic trajectories across human fetal brain development
Spiers, H., Hannon, E., Schalkwyk, L. C., Smith, R., Wong, C. C. Y., O’Donovan, M. C., Bray, N. J., & Mill, J. (2015) · Genome Research · doi:10.1101/gr.180273.114 · Cited by 287
DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis
Papanicolau‐Sengos, A., & Aldape, K. (2021) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathol-042220-022304 · Cited by 285
Single-cell epigenomics: techniques and emerging applications
Schwartzman, O., & Tanay, A. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3980 · Cited by 291
The molecular bases of the suicidal brain
Turecki, G. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3839 · Cited by 281
DNA methylation in mammalian development and disease
Smith, Z. D., Hetzel, S., & Meissner, A. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00760-8 · Cited by 310
Self-citationBanovich, N. E., Lan, X., McVicker, G., van de Geijn, B., Degner, J. F., Blischak, J., Roux, J., Pritchard, J. K., & Gilad, Y. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004663 · Cited by 291
Caloric restriction delays age-related methylation drift
Maegawa, S., Lu, Y., Tahara, T., Lee, J. T., Madzo, J., Liang, S., Jelinek, J., Colman, R. J., & Issa, J.-P. J. (2017) · Nature Communications · doi:10.1038/s41467-017-00607-3 · Cited by 270
Ronn, T., Volkov, P., Gillberg, L., Kokosar, M., Perfilyev, A., Jacobsen, A. L., Jorgensen, S. W., Brons, C., Jansson, P.-A., Eriksson, K.-F., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv124 · Cited by 270
Role of Tet proteins in enhancer activity and telomere elongationPreprint
Lu, F., Liu, Y., Jiang, L., Yamaguchi, S., & Zhang, Y. (2014) · Genes & Development · doi:10.1101/gad.248005.114 · Cited by 278
Biological embedding of experience: a primer on epigenetics
Aristizabal, M. J., Anreiter, I., Halldorsdottir, T., Odgers, C. L., McDade, T. W., Goldenberg, A., Mostafavi, S., Kobor, M. S., Binder, E. B., Sokolowski, M. B., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820838116 · Cited by 268
MethylC-seq library preparation for base-resolution whole-genome bisulfite sequencing
Urich, M. A., Nery, J. R., Lister, R., Schmitz, R. J., & Ecker, J. R. (2015) · Nature Protocols · doi:10.1038/nprot.2014.114 · Cited by 273
Promoter DNA Hypermethylation and Paradoxical Gene Activation
Smith, J., Sen, S., Weeks, R. J., Eccles, M. R., & Chatterjee, A. (2020) · Trends in cancer · doi:10.1016/j.trecan.2020.02.007 · Cited by 268
Non-CG Methylation in the Human Genome
He, Y., & Ecker, J. R. (2015) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090413-025437 · Cited by 280
DNA Methylation Dynamics of Human Hematopoietic Stem Cell Differentiation
Farlik, M., Halbritter, F., Müller, F., Choudry, F., Ebert, P., Klughammer, J., Farrow, S., Santoro, A., Ciaurro, V., Mathur, A., et al. (2016) · Cell stem cell · doi:10.1016/j.stem.2016.10.019 · Cited by 282
Self-citationBECon: a tool for interpreting DNA methylation findings from blood in the context of brain
Edgar, R. D., Jones, M. J., Meaney, M. J., Turecki, G., & Kobor, M. S. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.171 · Cited by 261
Zhang, X., Su, J., Jeong, M., Ko, M., Huang, Y., Park, H. J., Guzman, A., Lei, Y., Huang, Y.-H., Rao, A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3610 · Cited by 258
Ecker, J. R., Geschwind, D. H., Kriegstein, A. R., Ngai, J., Osten, P., Polioudakis, D., Regev, A., Šestan, N., Wickersham, I. R., & Zeng, H. (2017) · Neuron · doi:10.1016/j.neuron.2017.10.007 · Cited by 258
DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer
Teschendorff, A. E., Gao, Y., Jones, A., Ruebner, M., Beckmann, M. W., Wachter, D. L., Fasching, P. A., & Widschwendter, M. (2016) · Nature Communications · doi:10.1038/ncomms10478 · Cited by 252
Gilsbach, R., Schwaderer, M., Preißl, S., Grüning, B. A., Kranzhöfer, D., Schneider, P., Nührenberg, T. G., Mulero‐Navarro, S., Weichenhan, D., Braun, C., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02762-z · Cited by 251
Poirier, J. T., Gardner, E. E., Connis, N., Moreira, A. L., de Stanchina, E., Hann, C. L., & Rudin, C. M. (2015) · Oncogene · doi:10.1038/onc.2015.38 · Cited by 249
Genetic impacts on DNA methylation: research findings and future perspectives
Villicaña, S., & Bell, J. T. (2021) · Genome biology · doi:10.1186/s13059-021-02347-6 · Cited by 267
Yuan, M., Yang, B., Rothschild, G., Mann, J. J., Sanford, L. D., Tang, X., Huang, C., Wang, C., & Zhang, W. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01519-z · Cited by 246
“44,45 In addition, cytosines followed by a non-guanine base, such as cytosine, adenine, or thymine, might also experience DNA methylation.”
BackgroundEnhancer Function: Mechanistic and Genome-Wide Insights Come Together
Plank, J. L., & Dean, A. (2014) · Molecular Cell · doi:10.1016/j.molcel.2014.06.015 · Cited by 264
Transcriptional and epigenomic landscapes of CNS and non-CNS vascular endothelial cells
Sabbagh, M. F., Heng, J. S., Luo, C., Castanon, R. G., Nery, J. R., Rattner, A., Goff, L. A., Ecker, J. R., & Nathans, J. (2018) · eLife · doi:10.7554/elife.36187 · Cited by 245
Metadata
- Authors
- Ziller, M. J., Gu, H., Müller, F., Donaghey, J., Tsai, L. T.-Y., Kohlbacher, O., De Jager, P. L., Rosen, E. D., Bennett, D. A., Bernstein, B. E., Gnirke, A., & Meissner, A.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Beth Israel Deaconess Medical Center (US)
- Quantitative Biology Center (DE)
- Rush University Medical Center (US)
- Corresponding author
- Meissner, Alexander
- ORCID
- Venue
- Nature
- Published
- 2013-08
- Volume
- 500
- Issue
- 7463
- Pages
- 477-481
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3821869
- OpenAlex
- W2163327652
- Cited by
- 1,357
- References
- 30
- Citations by year
- 2026: 48 · 2025: 42 · 2024: 99 · 2023: 74 · 2022: 112 · 2021: 97 · 2020: 91 · 2019: 103 · 2018: 100 · 2017: 128 · 2016: 151 · 2015: 159 · 2014: 133 · 2013: 17
- FWCI
- 68.63
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Max Planck Digital Library (submittedVersion, other-oa)
- Funders
- National Institute on Aging (R01AG017917, R01AG36042, P30-AG 10161, R01‐AG17917, R01 AG15819, R01AG036042, R01AG015819, P30AG010161, RF1AG036042)
- National Institute of General Medical Sciences (P01GM099117)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK087092)
- National Institute of Environmental Health Sciences (U01ES017155, ES017690)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
- Keywords
- CpG methylation, cell type-specific methylation, differentially methylated regions, enhancers, transcription factor binding sites, gene regulatory elements, single nucleotide polymorphisms, epigenetic regulation, bisulfite sequencing, epigenome mapping
- MeSH terms
- Binding Sites, Enhancer Elements, Genetic, Humans, Organ Specificity, Sulfites, Transcription Factors, Genome, Human, Sequence Analysis, DNA, CpG Islands, DNA Methylation, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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