Abstract
Studies of DNA methylation in Arabidopsis have rapidly advanced from the analysis of a single reference accession to investigations of large populations. The goal of emerging population studies is to detect differentially methylated regions (DMRs) at the genome-wide scale, and to relate this variation to gene expression and phenotypic diversity.
Whole-genome bisulfite sequencing (WGBS-seq) has established itself as a gold standard in DNA methylation analysis due to its high accuracy and single cytosine measurement resolution. However, scaling up the use of this technology for large population studies is currently not only cost prohibitive but also poses nontrivial bioinformatic challenges. If the end-point of the study is to detect DMRs at the level of several hundred base pairs rather than at the level of single cytosines, low-resolution array-based methods, such as MeDIP-chip, may be entirely sufficient. However, the trade-off between measurement accuracy and experimental/analytical practicality needs to be weighted carefully. To help make such experimental choices, we conducted a side-by-side comparison between the popular dual-channel MeDIP-chip Nimblegen technology and Illumina WGBS-seq in two independent Arabidopsis lines.
Our analysis shows that MeDIP-chip performs reasonably well in detecting DNA methylation at probe-level resolution, yielding a genome-wide combined false-positive and false-negative rate of about 0.21. However, detection can be susceptible to strong signal distortions resulting from a combination of dye bias and the CG content of effectively unmethylated genomic regions. We show that these issues can be easily bypassed by taking appropriate data preparation steps and applying suitable analysis tools.
We conclude that MeDIP-chip is a reasonable alternative to WGBS-seq in emerging Arabidopsis population epigenetic studies.
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References
Law JA, Jacobsen SE (2010) Establishing, maintaining and modifying DNA methylation patterns in plants and animals. Nat Rev Genet 11:204–220
Laird PW (2010) Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet 11:191–203
Zhang X, Yazaki J, Sundaresan A, Cokus S, Chan SW, Chen H (2006) Genome-wide high-resolution mapping and functional analysis of DNA methylation in Arabidopsis. Cell 126:1189–1201
Zilberman D, Gehring M, Tran RK et al (2007) Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nat Genet 39:61–69
Lister R, O’Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, Ecker JR (2008) Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133:523–536
Cokus SJ, Feng S, Zhang X, Chen Z, Merriman B, Haudenschild CD et al (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452:215–219
Schmitz RJ, Schultz MD, Lewsey MG et al (2011) Transgenerational epigenetic instability is a source of novel methylation variants. Science 334:369–373
Becker C, Hagmann J, Müller J et al (2011) Spontaneous epigenetic variation in the Arabidopsis thaliana methylome. Nature 480:245–249
Boyes J, Bird A (1992) Repression of genes by DNA methylation depends on CpG density and promoter strength: evidence for involvement of a methyl-CpG binding protein. EMBO J 11:327–333
Lorincz MC, Schübeler D, Hutchinson SR, Dickerson DR, Groudine M (2002) DNA methylation density influences the stability of an epigenetic imprint and Dnmt3a/b-independent de novo methylation. Mol Cell Biol 22:7572–7580
Johannes F, Porcher E, Teixeira F, Saliba-Colombani V, Simon M, Agier N et al (2009) Assessing the impact of transgenerational epigenetic variation on complex traits. PLoS Genet 5:e1000530
Colomé-Tatché M, Cortijo S, Wardenaar R, Morgado L, Lahouze B, Sarazin A et al (2012) Features of the Arabidopsis recombination landscape resulting from the combined loss of sequence variation and DNA methylation. Proc Natl Acad Sci USA 109:16240–16245
Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, Lam WL, Schübeler D (2005) Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat Genet 37:853–862
Down TA, Rakyan VK, Turner DJ, Flicek P, Li H, Kulesha E et al (2008) A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol 26:779–785
Cortijo S, Wardenaar R, Colomé-Tatché M, Johannes F, Roudier F, Colot V (2012) Genome-wide analysis of DNA methylation in Arabidopsis using MeDIP-chip. Methods Mol Biol 15:2930–2939
Frommer M, McDonald LE, Millar DS, Collis CM, Watt F, Grigg GW et al (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci USA 89:1827–1831
Clark SJ, Harrison J, Paul CL, Frommer M (1994) High sensitivity mapping of methylated cytosines. Nucleic Acids Res 22:2990–2997
Lister R, Ecker JR (2009) Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res 19:959–966
Krueger F, Kreck B, Franke A, Andrews SR (2012) DNA methylome analysis using short bisulfite sequencing data. Nat Methods 9:145–151
Chen PY, Cokus SJ, Pellegrini M (2010) BS Seeker: precise mapping for bisulfite sequencing. BMC Bioinformatics 11:203
Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19:185–193
R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, ISBN: 3-900051-07-0. http://www.R-project.org
Dobbin KK, Kawasaki ES, Petersen DW, Simon RM (2005) Characterizing dye bias in microarray experiments. Bioinformatics 21:2430–2437
Dombkowski AA, Thibodeau BJ, Starcevic SL, Novak RF (2004) Gene-specific dye bias in microarray reference designs. FEBS Lett 560:120–124
Martin-Magniette ML, Mary-Huard T, Bérard C, Robin S (2008) ChIPmix: mixture model of regressions for two-color ChIP-chip analysis. Bioinformatics 24:i181–i186
Andrews S (2007) ChIPmonk: software for viewing and analysing ChIP-on-chip data. BMC Syst Biol 1(Suppl 1):P80
Johannes F, Wardenaar R, Colomé-Tatché M, Mousson F, de Graaf P, Mokry M et al (2010) Comparing genome-wide chromatin profiles using ChIP-chip or ChIP-seq. Bioinformatics 26:1000–1006
Seifert M, Cortijo S, Colomé-Tatché M, Johannes F, Roudier F, Colot V (2012) MeDIP-HMM: genome-wide identification of distinct DNA methylation states from high-density tiling arrays. Bioinformatics 28:2930–2939
Li W, Meyer CA, Liu XS (2005) A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences. Bioinformatics 21(Suppl 1):i274–i282
Royce TE, Rozowsky JS, Gerstein MB (2007) Assessing the need for sequence-based normalization in tiling microarray experiments. Bioinformatics 23:988–997
Gilbert D, Rechtsteiner A (2009) Comments on sequence normalization of tiling array expression. Bioinformatics 25:2171–2173
Acknowledgements
This work was supported by grants from the Netherlands Organization for Scientific Research (NWO) (to F.J. and M.C.-T) and the Netherlands Bioinformatics Centre (NBIC) (to R.W.). Work in the Colot lab is supported in part by the European Union Network of Excellence EpigeneSys.
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Wardenaar, R., Liu, H., Colot, V., Colomé-Tatché, M., Johannes, F. (2013). Evaluation of MeDIP-Chip in the Context of Whole-Genome Bisulfite Sequencing (WGBS-Seq) in Arabidopsis . In: Lee, TL., Shui Luk, A. (eds) Tiling Arrays. Methods in Molecular Biology, vol 1067. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-607-8_13
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DOI: https://doi.org/10.1007/978-1-62703-607-8_13
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