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Bioinformatic Estimation of DNA Methylation and Hydroxymethylation Proportions

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TET Proteins and DNA Demethylation

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2272))

Abstract

Simultaneous measurement of 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) at the single-nucleotide level can be obtained by combining data from DNA processing methods including traditional bisulfite (BS), oxidative bisulfite (oxBS), or Tet-assisted (TAB) bisulfite conversion. Array-based technologies have been widely used in this task, due to their time and cost efficiency. For methylation studies using BS data, many protocols and related packages have been suggested in the literature to deal with limitations and confounders that arise from array data. In this chapter, we illustrate how the reader can make small adjustments to these protocols to obtain estimates of methylation and hydroxymethylation proportions.

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References

  1. Booth MJ, Branco MR, Ficz G et al (2012) Quantitative sequencing of 5-methylcytosine and 5-hydroxymethylcytosine at single-base resolution. Science 336:934–937. https://doi.org/10.1126/science.1220671

    Article  CAS  PubMed  Google Scholar 

  2. Yu M, Hon G, Szulwach K et al (2012) Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149:1368–1380. https://doi.org/10.1016/j.cell.2012.04.027

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Nazor KL, Boland MJ, Bibikova M et al (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. https://doi.org/10.1016/j.ygeno.2014.08.014

    Article  CAS  PubMed  Google Scholar 

  4. Field DAB, Sarah F, Beraldi (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. https://doi.org/10.1371/journal.pone.0118202

    Article  CAS  Google Scholar 

  5. Stewart SK, Morris TJ, Guilhamon P et al (2015) OxBS-450K: a method for analysing hydroxymethylation using 450K beadchips. Methods 72:9–15. https://doi.org/10.1016/j.ymeth.2014.08.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Qu J, Zhou M, Song Q et al (2013) MLML: consistent simultaneous estimates of dna methylation and hydroxymethylation. Bioinformatics 29:2645–2646. https://doi.org/10.1093/bioinformatics/btt459

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Xu Z, Taylor JA, Leung Y-K et al (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. https://doi.org/10.1093/bioinformatics/btw527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Houseman EA, Johnson KC, Christensen BC (2016) OxyBS: estimation of 5-methylcytosine and 5-hydroxymethylcytosine from tandem-treated oxidative bisulfite and bisulfite DNA. Bioinformatics 32:2505–2507. https://doi.org/10.1093/bioinformatics/btw158

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kiihl SF, Martinez-Garrido MJ, Domingo-Relloso A et al (2019) MLML2R: an r package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions. Stat Appl Genet Mol Biol 18. https://doi.org/10.1515/sagmb-2018-0031

  10. R Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna

    Google Scholar 

  11. Huber W, Carey VJ, Gentleman R et al (2015) Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods 12:115–121

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Aryee MJ, Jaffe AE, Corrada-Bravo H et al (2014) Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30:1363–1369. https://doi.org/10.1093/bioinformatics/btu049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Davis S, Meltzer P (2007) GEOquery: a bridge between the gene expression omnibus (geo) and bioconductor. Bioinformatics 14:1846–1847

    Article  Google Scholar 

  14. Zeng C, Zhang Z, Wang J et al (2019) Application of the high-throughput tab-array for the discovery of novel 5-hydroxymethylcytosine biomarkers in pancreatic ductal adenocarcinoma. Epigenomes 3:16. https://doi.org/10.3390/epigenomes3030016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Skvortsova K, Zotenko E, Luu P-L et al (2017) Comprehensive evaluation of genome-wide 5-hydroxymethylcytosine profiling approaches in human DNA. Epigenetics Chromatin 10:16. https://doi.org/10.1186/s13072-017-0123-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Du P, Zhang X, Huang C-C et al (2010) Comparison of beta-value and m-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11:587. https://doi.org/10.1186/1471-2105-11-587

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Maksimovic J, Phipson B, Oshlack A (2017) A cross-package bioconductor workflow for analysing methylation array data [version 3; referees: 4 approved]. F1000Research 5:1281. https://doi.org/10.12688/f1000research.8839.3

    Article  PubMed Central  Google Scholar 

  18. Fortin J-P, Labbe A, Lemire M et al (2014) Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol 15:503. https://doi.org/10.1186/s13059-014-0503-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Teschendorff AE, Marabita F, Lechner M et al (2012) A beta-mixture quantile normalization method for correcting probe design bias in illumina infinium 450 k DNA methylation data. Bioinformatics 29:189–196. https://doi.org/10.1093/bioinformatics/bts680

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Niu L, Xu Z, Taylor JA (2016) RCP: a novel probe design bias correction method for illumina methylation BeadChip. Bioinformatics 32:2659–2663. https://doi.org/10.1093/bioinformatics/btw285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Triche TJ, Weisenberger DJ, Van Den Berg D et al (2013) Low-level processing of Illumina Infinium DNA methylation BeadArrays. Nucleic Acids Res 41:e90. https://doi.org/10.1093/nar/gkt090

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Maksimovic J, Gordon L, Oshlack A (2012) SWAN: subset-quantile within array normalization for illumina infinium HumanMethylation450 BeadChips. Genome Biol 13:R44. https://doi.org/10.1186/gb-2012-13-6-r44

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Liu J, Siegmund KD (2016) An evaluation of processing methods for HumanMethylation450 BeadChip data. BMC Genomics 17:469. https://doi.org/10.1186/s12864-016-2819-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pidsley R, Zotenko E, Peters TJ et al (2016) Critical evaluation of the illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling. Genome Biol 17:208. https://doi.org/10.1186/s13059-016-1066-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chen Y-A, Lemire M, Choufani S et al (2013) Discovery of cross-reactive probes and polymorphic CpGs in the illumina infinium HumanMethylation450 microarray. Epigenetics 8:203–209. https://doi.org/10.4161/epi.23470

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Oytam Y, Sobhanmanesh F, Duesing K et al (2016) Risk-conscious correction of batch effects: maximising information extraction from high-throughput genomic datasets. BMC Bioinformatics 17:1–17. https://doi.org/10.1186/s12859-016-1212-5

    Article  Google Scholar 

  27. Johnson WE, Li C, Rabinovic A (2006) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127. https://doi.org/10.1093/biostatistics/kxj037

    Article  PubMed  Google Scholar 

  28. Leek JT, Johnson WE, Parker HS et al (2012) The SVA package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882–883. https://doi.org/10.1093/bioinformatics/bts034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3:e161. https://doi.org/10.1371/journal.pgen.0030161

    Article  CAS  PubMed Central  Google Scholar 

  30. Gagnon-Bartsch JA, Speed TP (2011) Using control genes to correct for unwanted variation in microarray data. Biostatistics 13:539–552. https://doi.org/10.1093/biostatistics/kxr034

    Article  PubMed  Google Scholar 

  31. Salas LA, Koestler DC, Butler RA et al (2018) An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the illumina HumanMethylationEPIC BeadArray. Genome Biol 19:64. https://doi.org/10.1186/s13059-018-1448-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Salas LA, Koestler DC (2019) FlowSorted.Blood.EPIC: Illumina EPIC data on immunomagnetic sorted peripheral adultblood cells. Bioconductor. https://doi.org/10.18129/B9.bioc.FlowSorted.Blood.EPIC

  33. Houseman EA, Accomando WP, Koestler DC et al (2012) DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13:86. https://doi.org/10.1186/1471-2105-13-86

    Article  PubMed  PubMed Central  Google Scholar 

  34. Jaffe AE (2019) FlowSorted.Blood.450k: Illumina humanmethylation data on sorted blood cell populations. Bioconductor. https://doi.org/10.18129/B9.bioc.FlowSorted.Blood.450k

  35. Hicks SC, Irizarry RA (2019) methylCC: technology-independent estimation of cell type composition using differentially methylated regions. Genome Biol 20:261. https://doi.org/10.1186/s13059-019-1827-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Correspondence to Samara Flamini Kiihl .

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Kiihl, S.F. (2021). Bioinformatic Estimation of DNA Methylation and Hydroxymethylation Proportions. In: Bogdanovic, O., Vermeulen, M. (eds) TET Proteins and DNA Demethylation. Methods in Molecular Biology, vol 2272. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1294-1_8

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  • DOI: https://doi.org/10.1007/978-1-0716-1294-1_8

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1293-4

  • Online ISBN: 978-1-0716-1294-1

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