Abstract
Accurately measuring epigenetic marks such as 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level, requires combining data from DNA processing methods including traditional (BS), oxidative (oxBS) or Tet-Assisted (TAB) bisulfite conversion. We introduce the R package
-
Äijö, T., Y. Huang, H. Mannerström, L. Chavez, A. Tsagaratou, A. Rao and H. Lähdesmäki (2016): “A probabilistic generative model for quantification of dna modifications enables analysis of demethylation pathways,” Genome Biol., 17, 49.
-
Aryee, M. J., A. E. Jaffe, H. Corrada-Bravo, C. Ladd-Acosta, A. P. Feinberg, K. D. Hansen and R. A. Irizarry (2014): “Minfi: A flexible and comprehensive Bioconductor package for the analysis of Infinium DNA Methylation microarrays,” Bioinformatics, 30, 1363–1369.
-
Ayer, M., H. D. Brunk, G. M. Ewing, W. T. Reid and E. Silverman (1955): “An empirical distribution function for sampling with incomplete information,” Ann. Math. Statist., 26, 641–647.
-
Booth, M. J., M. R. Branco, G. Ficz, D. Oxley, F. Krueger, W. Reik and S. Balasubramanian (2012): “Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution,” Science, 336, 934–937.
-
Feinberg, A. P. (2018): “The key role of epigenetics in human disease prevention and mitigation,” N. Engl. J. Med., 378, 1323–1334.
-
Field, S. F., D. Beraldi, M. Bachman, S. K. Stewart, S. Beck and S. Balasubramanian (2015): “Accurate measurement of 5-methylcytosine and 5-hydroxymethylcytosine in human cerebellum dna by oxidative bisulfite on an array (oxbs-array),” PLoS One, 10, 1–12.
-
Houseman, E. A., K. C. Johnson and B. C. Christensen (2016): “Oxybs: estimation of 5-methylcytosine and 5-hydroxymethylcytosine from tandem-treated oxidative bisulfite and bisulfite dna,” Bioinformatics, 32, 2505–2507.
-
Huang, Y., W. A. Pastor, Y. Shen, M. Tahiliani, D. R. Liu and A. Rao (2010): “The behaviour of 5-hydroxymethylcytosine in bisulfite sequencing,” PLoS One, 5, 1–9.
-
Leek, J. T., W. E. Johnson, H. S. Parker, E. J. Fertig, A. E. Jaffe and J. D. Storey (2016): sva: Surrogate Variable Analysis, R package version 3.22.0.
-
Li, X., Y. Liu, T. Salz, K. D. Hansen and A. Feinberg (2016): “Whole-genome analysis of the methylome and hydroxymethylome in normal and malignant lung and liver,” Genome Res., 26, 1730–1741.
-
Mariani, C., J. Madzo, E. Moen, A. Yesilkanal and L. Godley (2013): “Alterations of 5-hydroxymethylcytosine in human cancers,” Cancers, 5, 786–814.
-
Nazor, K. L., M. J. Boland, M. Bibikova, B. Klotzle, M. Yu, V. L. Glenn-Pratola, J. P. Schell, R. L. Coleman, M. C. C. da Silva, U. Schmidt, S. E. Peterson, C. He, J. F. Loring and J.-B. Fan (2014): “Application of a low cost array-based technique – TAB-array – for quantifying and mapping both 5mc and 5hmc at single base resolution in human pluripotent stem cells,” Genomics, 104, 358–367.
-
Pfeifer, G. P., W. Xiong, M. A. Hahn and S.-G. Jin (2014): “The role of 5-hydroxymethylcytosine in human cancer,” Cell Tissue Res., 356, 631–641.
-
Qu, J., M. Zhou, Q. Song, E. E. Hong and A. D. Smith (2013): “Mlml: consistent simultaneous estimates of dna methylation and hydroxymethylation,” Bioinformatics, 29, 2645–2646.
-
Ritchie, M. E., B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi and G. K. Smyth (2015): “limma powers differential expression analyses for RNA-sequencing and microarray studies,” Nucleic Acids Res., 43, e47.
-
Stewart, S. K., T. J. Morris, P. Guilhamon, H. Bulstrode, M. Bachman, S. Balasubramanian and S. Beck (2015): “oxbs-450k: A method for analysing hydroxymethylation using 450k beadchips,” Methods, 72(Supplement C), 9–15. (Epi)Genomics approaches and their applications.
-
Tahiliani, M., K. P. Koh, Y. Shen, W. A. Pastor, H. Bandukwala, Y. Brudno, S. Agarwal, L. M. Iyer, D. R. Liu, L. Aravind and A. Rao (2009): “Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian dna by mll partner tet1,” Science, 324, 930–935.
-
Thienpont, B., J. Steinbacher, H. Zhao, F. D’Anna, A. Kuchnio, A. Ploumakis, B. Ghesquière, L. V. Dyck, B. Boeckx, L. Schoonjans, E. Hermans, F. Amant, V. N. Kristensen, K. P. Koh, M. Mazzone, M. L. Coleman, T. Carell, P. Carmeliet and D. Lambrechts (2016): “Tumour hypoxia causes DNA hypermethylation by reducing TET activity,” Nature, 537, 63–68.
-
Xu, Z., J. A. Taylor, Y.-K. Leung, S.-M. Ho and L. Niu (2016): “oxbs-mle: an efficient method to estimate 5-methylcytosine and 5-hydroxymethylcytosine in paired bisulfite and oxidative bisulfite treated dna,” Bioinformatics, 32, 3667–3669.
-
Yu, M., G. Hon, K. Szulwach, C.-X. Song, L. Zhang, A. Kim, X. Li, Q. Dai, Y. Shen, B. Park, J.-H. Min, P. Jin, B. Ren and C. He (2012): “Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome,” Cell, 149, 1368–1380.
This work was supported by the Strategic Action for Research in Health Sciences from the Institute of Health Carlos III [CP12/03080; PI15/00071]. The Strategic Action for Research in Health Sciences is an initiative from Carlos III Health Institute Madrid and the Spanish Ministry of Economy and Competitiveness and is co-funded with European Funds for Regional Development (FEDER). The work of Samara Kiihl was supported by FAPESP-Brazil [Funder Id: 10.13039/501100001807, 2013/00506-1; 2014/03374-1].
©2019 Walter de Gruyter GmbH, Berlin/Boston.
