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Detecting DNA cytosine methylation using nanopore sequencing
Open accessAbstract
In nanopore sequencing devices, electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine (5-mC). Here we quantified the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. By using synthetically methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytosine. We applied our method to sequence the methylome of human DNA, without requiring special steps for library preparation.
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Cites
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
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
DNA methylation arrays as surrogate measures of cell mixture distribution
Houseman, E. A., Accomando, W. P., Koestler, D. C., Christensen, B. C., Marsit, C. J., Nelson, H. H., Wiencke, J. K., & Kelsey, K. T. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-86 · Cited by 3,175
ChIP–seq: advantages and challenges of a maturing technology
Park, P. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2641 · Cited by 2,217
The NIH Roadmap Epigenomics Mapping Consortium
Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1010-1045 · Cited by 1,892
Direct detection of DNA methylation during single-molecule, real-time sequencing
Flusberg, B. A., Webster, D. R., Lee, J. H., Travers, K. J., Olivares, E. C., Clark, T. A., Korlach, J., & Turner, S. W. (2010) · Nature Methods · doi:10.1038/nmeth.1459 · Cited by 1,460
DNA methylation on N6-adenine in mammalian embryonic stem cells
Wu, T. P., Wang, T., Seetin, M. G., Lai, Y., Zhu, S., Lin, K., Liu, Y., Byrum, S. D., Mackintosh, S. G., Zhong, M., et al. (2016) · Nature · doi:10.1038/nature17640 · Cited by 648
Detection and mapping of 5-methylcytosine and 5-hydroxymethylcytosine with nanopore MspA
Laszlo, A. H., Derrington, I. M., Brinkerhoff, H., Langford, K. W., Nova, I. C., Samson, J. M., Bartlett, J. J., Pavlenok, M., & Gundlach, J. H. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310240110 · Cited by 321
Landan, G., Cohen, N. M., Mukamel, Z., Bar, A., Molchadsky, A., Brosh, R., Horn-Saban, S., Zalcenstein, D. A., Goldfinger, N., Zundelevich, A., et al. (2012) · Nature Genetics · doi:10.1038/ng.2442 · Cited by 305
Modeling methyl-sensitive transcription factor motifs with an expanded epigenetic alphabet
Viner, C., Ishak, C. A., Johnson, J., Walker, N. J., Shi, H., Sjöberg-Herrera, M. K., Shen, S. Y., Lardo, S. M., Adams, D. J., Ferguson-Smith, A. C., et al. (2016) · doi:10.1101/043794 · Cited by 18
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Jain, M., Olsen, H. E., Paten, B., & Akeson, M. (2016) · Genome Biology · doi:10.1186/s13059-016-1103-0 · Cited by 1,588
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Miga, K. H., Koren, S., Rhie, A., Vollger, M. R., Gershman, A., Bzikadze, A., Brooks, S., Howe, E., Porubsky, D., Logsdon, G. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2547-7 · Cited by 765
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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
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Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401
Segmental duplications and their variation in a complete human genome
Vollger, M. R., Guitart, X., Dishuck, P. C., Mercuri, L., Harvey, W. T., Gershman, A., Diekhans, M., Sulovari, A., Munson, K. M., Lewis, A. P., et al. (2022) · Science · doi:10.1126/science.abj6965 · Cited by 350
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Gershman, A., Sauria, M. E. G., Guitart, X., Vollger, M. R., Hook, P. W., Hoyt, S. J., Jain, M., Shumate, A., Razaghi, R., Koren, S., et al. (2022) · Science · doi:10.1126/science.abj5089 · Cited by 297
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Rajamäki, K., Taira, A., Katainen, R., Välimäki, N., Kuosmanen, A., Plaketti, R.-M., Seppälä, T. T., Ahtiainen, M., Wirta, E.-V., Vartiainen, E., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.04.042 · Cited by 230
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Shipony, Z., Marinov, G. K., Swaffer, M. P., Sinnott-Armstrong, N. A., Skotheim, J. M., Kundaje, A., & Greenleaf, W. J. (2020) · Nature Methods · doi:10.1038/s41592-019-0730-2 · Cited by 160
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Zhang, H., Jain, C., & Aluru, S. (2020) · BMC Genomics · doi:10.1186/s12864-020-07227-0 · Cited by 150
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Chen, X., Xu, H., Shu, X., & Song, C.-X. (2025) · Cell Death & Differentiation · doi:10.1038/s41418-023-01213-1 · Cited by 105
Cellular hallmarks of aging emerge in the ovary prior to primordial follicle depletion
Ansere, V. A., Ali-Mondal, S., Sathiaseelan, R., Garcia, D. N., Isola, J. V. V., Henseb, J. D., Saccon, T. D., Ocañas, S. R., Tooley, K. B., Stout, M. B., et al. (2021) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2020.111425 · Cited by 101
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Schmid, M., Frei, D., Patrignani, A., Schlapbach, R., Frey, J. E., Remus-Emsermann, M. N. P., & Ahrens, C. H. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky726 · Cited by 92
Detection of ribonucleotides embedded in DNA by Nanopore sequencing
Grasso, L., Fonzino, A., Manzari, C., Leonardi, T., Picardi, E., Gissi, C., Lazzaro, F., Pesole, G., & Muzi-Falconi, M. (2024) · Communications Biology · doi:10.1038/s42003-024-06077-w · Cited by 10
Metadata
- Authors
- Simpson, J. T., Workman, R. E., Zuzarte, P. C., David, M., Dursi, L. J., & Timp, W.
- Venue
- Nature Methods
- Published
- 2017-02-20
- Volume
- 14
- Issue
- 4
- Pages
- 407-410
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2591367881
- Cited by
- 1,136
- References
- 10
- FWCI
- 36.66
- Type
- journal article
- Language
- en
- Open access
- green
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