The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Genomic Distribution and Inter-Sample Variation of Non-CpG Methylation across Human Cell Types
Open accessAbstract
DNA methylation plays an important role in development and disease. The primary sites of DNA methylation in vertebrates are cytosines in the CpG dinucleotide context, which account for roughly three quarters of the total DNA methylation content in human and mouse cells. While the genomic distribution, inter-individual stability, and functional role of CpG methylation are reasonably well understood, little is known about DNA methylation targeting CpA, CpT, and CpC (non-CpG) dinucleotides. Here we report a comprehensive analysis of non-CpG methylation in 76 genome-scale DNA methylation maps across pluripotent and differentiated human cell types. We confirm non-CpG methylation to be predominantly present in pluripotent cell types and observe a decrease upon differentiation and near complete absence in various somatic cell types. Although no function has been assigned to it in pluripotency, our data highlight that non-CpG methylation patterns reappear upon iPS cell reprogramming. Intriguingly, the patterns are highly variable and show little conservation between different pluripotent cell lines. We find a strong correlation of non-CpG methylation and DNMT3 expression levels while showing statistical independence of non-CpG methylation from pluripotency associated gene expression. In line with these findings, we show that knockdown of DNMTA and DNMT3B in hESCs results in a global reduction of non-CpG methylation. Finally, non-CpG methylation appears to be spatially correlated with CpG methylation. In summary these results contribute further to our understanding of cytosine methylation patterns in human cells using a large representative sample set.
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The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
John Lu, Z. Q. (2010) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,390
WebLogo: A Sequence Logo Generator: Figure 1
Crooks, G. E., Hon, G., Chandonia, J.-M., & Brenner, S. E. (2004) · Genome Research · doi:10.1101/gr.849004 · Cited by 12,386
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
Genome-wide maps of chromatin state in pluripotent and lineage-committed cells
Mikkelsen, T. S., Ku, M., Jaffe, D. B., Issac, B., Lieberman, E., Giannoukos, G., Alvarez, P., Brockman, W., Kim, T.-K., Koche, R. P., et al. (2007) · Nature · doi:10.1038/nature06008 · Cited by 4,129
Self-citationEstablishing, maintaining and modifying DNA methylation patterns in plants and animals
Law, J. A., & Jacobsen, S. E. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2719 · Cited by 3,850
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
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Cited by
DNA methylation: roles in mammalian development
Smith, Z. D., & Meissner, A. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3354 · Cited by 3,045
Self-citationGlobal epigenomic reconfiguration during mammalian brain development
Lister, R., Mukamel, E. A., Nery, J. R., Urich, M., Puddifoot, C. A., Johnson, N. D., Lucero, J., Huang, Y., Dwork, A. J., Schultz, M. D., et al. (2013) · Science · doi:10.1126/science.1237905 · Cited by 1,830
Distribution, recognition and regulation of non-CpG methylation in the adult mammalian brain
Guo, J. U., Su, Y., Shin, J. H., Shin, J., Li, H., Xie, B., Zhong, C., Hu, S., Le, T., Fan, G., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3607 · Cited by 714
Xie, W., Barr, C. L., Kim, A., Yue, F., Lee, A. Y., Eubanks, J., Dempster, E. L., & Ren, B. (2012) · Cell · doi:10.1016/j.cell.2011.12.035 · Cited by 543
DNA methylation: An epigenetic mark of cellular memory
Kim, M., & Costello, J. (2017) · Experimental & Molecular Medicine · doi:10.1038/emm.2017.10 · Cited by 450
Metadata
- Authors
- Ziller, M. J., Müller, F., Liao, J., Zhang, Y., Gu, H., Bock, C., Boyle, P., Epstein, C. B., Bernstein, B. E., Lengauer, T., Gnirke, A., & Meissner, A.
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-12-08
- Volume
- 7
- Issue
- 12
- Pages
- e1002389
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- OpenAlex
- W2130222411
- Cited by
- 396
- References
- 37
- FWCI
- 11.17
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ziller, M. J., Müller, F., Liao, J., Zhang, Y., Gu, H., Bock, C., Boyle, P., Epstein, C. B., Bernstein, B. E., Lengauer, T., Gnirke, A., & Meissner, A. (2011). Genomic Distribution and Inter-Sample Variation of Non-CpG Methylation across Human Cell Types. PLoS Genetics. https://doi.org/10.1371/journal.pgen.1002389
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