Key Points
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Methylation of cytosine residues at the carbon 5 position occurs naturally in many bacteria, archaea and eukaryotic species, in which it has various roles in protecting the genome from invading genomic parasites or in controlling the expression potential of regions of the genome.
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DNA methylation is established after DNA synthesis by dedicated enzymes with specific target sequence recognition sites. The uneven distribution of target sites and sample heterogeneity can result in complex DNA methylation patterns.
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The genomic distribution of DNA methylation encodes important biological information. Hence, techniques for comprehensively describing DNA methylation patterns have been developed.
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Many standard molecular biology techniques, such as cloning and PCR, erase DNA methylation information, and hybridization does not distinguish between methylated and unmethylated cytosines.
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There are three different initial treatments of DNA that can be used to reveal DNA methylation: endonuclease digestion, affinity enrichment and bisulphite conversion.
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The implementation of array hybridization techniques greatly facilitated genome-scale analysis of DNA methylation. Endonuclease-treated or affinity-enriched DNA methods are particularly well suited for array hybridization, whereas bisulphite conversion techniques are not.
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Next-generation sequencing allows for whole-genome single-base-pair resolution characterization of DNA methylation patterns, particularly as applied to bisulphite-converted DNA.
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No single technique excels in all aspects. Sample number and characteristics, as well as the desired accuracy, coverage and resolution, influence the choice of technique.
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DNA methylation is usually measured on a β-distributed absolute scale from 0 to 1, or 0 to 100%, rather than on an infinite scale of log ratios.
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The unique data distribution characteristics of DNA methylation will require the development of dedicated bioinformatics and computational tools.
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Single-molecule and nanopore sequencing approaches are likely to usher in the next revolution in high-throughput DNA methylation analysis.
Abstract
Methylation of cytosine bases in DNA provides a layer of epigenetic control in many eukaryotes that has important implications for normal biology and disease. Therefore, profiling DNA methylation across the genome is vital to understanding the influence of epigenetics. There has been a revolution in DNA methylation analysis technology over the past decade: analyses that previously were restricted to specific loci can now be performed on a genome-scale and entire methylomes can be characterized at single-base-pair resolution. However, there is such a diversity of DNA methylation profiling techniques that it can be challenging to select one. This Review discusses the different approaches and their relative merits and introduces considerations for data analysis.
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References
Zhang, X. et al. Genome-wide high-resolution mapping and functional analysis of DNA methylation in Arabidopsis. Cell 126, 1189–1201 (2006). This study provided the first comprehensive DNA methylation analysis of a eukaryotic genome using whole-genome tiling arrays on affinity-enriched DNA.
Zilberman, D., Gehring, M., Tran, R. K., Ballinger, T. & Henikoff, S. Genome-wide analysis of Arabidopsis thaliana DNA methylation uncovers an interdependence between methylation and transcription. Nature Genet. 39, 61–69 (2007).
Zhang, X., Shiu, S., Cal, A. & Borevitz, J. O. Global analysis of genetic, epigenetic and transcriptional polymorphisms in Arabidopsis thaliana using whole genome tiling arrays. PLoS Genet. 4, e1000032 (2008).
Jones, P. A. The DNA methylation paradox. Trends Genet. 15, 34–37 (1999).
Hellman, A. & Chess, A. Gene body-specific methylation on the active X chromosome. Science 315, 1141–1143 (2007).
Ball, M. P. et al. Targeted and genome-scale strategies reveal gene-body methylation signatures in human cells. Nature Biotech. 27, 361–368 (2009).
Miura, A. et al. An Arabidopsis jmjC domain protein protects transcribed genes from DNA methylation at CHG sites. EMBO J. 28, 1078–1086 (2009).
Costello, J. F. et al. Aberrant CpG-island methylation has non-random and tumour-type-specific patterns. Nature Genet. 24, 132–138 (2000).
Allegrucci, C. et al. Restriction landmark genome scanning identifies culture-induced DNA methylation instability in the human embryonic stem cell epigenome. Hum. Mol. Genet. 16, 1253–1268 (2007).
Kawai, J. et al. Methylation profiles of genomic DNA of mouse developmental brain detected by restriction landmark genomic scanning (RLGS) method. Nucleic Acids Res. 21, 5604–5608 (1993).
Plass, C. et al. Identification of Grf1 on mouse chromosome 9 as an imprinted gene by RLGS-M. Nature Genet. 14, 106–109 (1996).
Song, F. et al. Association of tissue-specific differentially methylated regions (TDMs) with differential gene expression. Proc. Natl Acad. Sci. USA 102, 3336–3341 (2005).
Hayashizaki, Y. et al. Restriction landmark genomic scanning method and its various applications. Electrophoresis 14, 251–258 (1993). This study demonstrated the principle of genome-scale DNA methylation analysis using RLGS.
Hatada, I. et al. A microarray-based method for detecting methylated loci. J. Hum. Genet. 47, 448–451 (2002).
Balog, R. P. et al. Parallel assessment of CpG methylation by two-color hybridization with oligonucleotide arrays. Anal. Biochem. 309, 301–310 (2002).
van Steensel, B., Delrow, J. & Henikoff, S. Chromatin profiling using targeted DNA adenine methyltransferase. Nature Genet. 27, 304–308 (2001).
Yan, P. S. et al. CpG island arrays: an application toward deciphering epigenetic signatures of breast cancer. Clin. Cancer Res. 6, 1432–1438 (2000).
Huang, T. H., Perry, M. R. & Laux, D. E. Methylation profiling of CpG islands in human breast cancer cells. Hum. Mol. Genet. 8, 459–470 (1999).
El-Osta, A. & Wolffe, A. P. Analysis of chromatin-immunopurified MeCP2-associated fragments. Biochem. Biophys. Res. Commun. 289, 733–737 (2001).
Beck, S., Olek, A. & Walter, J. From genomics to epigenomics: a loftier view of life. Nature Biotech. 17, 1144 (1999).
Yan, P. S. et al. Dissecting complex epigenetic alterations in breast cancer using CpG island microarrays. Cancer Res. 61, 8375–8380 (2001).
Cokus, S. J. et al. Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452, 215–219 (2008).
Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).
Lister, R. et al. Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133, 523–536 (2008). References 22–24 provided the first single-base-pair resolution WGSBS of the A. thaliana (references 22 and 24) and human (reference 23) genomes.
Deng, J. et al. Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming. Nature Biotech. 27, 353–360 (2009).
Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766–770 (2008). This study provided the first genome-scale single-base-pair resolution DNA methylation map of mammalian genomes by RRBS.
Kriaucionis, S. & Heintz, N. The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324, 929–930 (2009).
Tahiliani, M. et al. Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324, 930–935 (2009).
Laird, P. W. The power and the promise of DNA methylation markers. Nature Rev. Cancer 3, 253–266 (2003).
Schones, D. E. & Zhao, K. Genome-wide approaches to studying chromatin modifications. Nature Rev. Genet. 9, 179–191 (2008).
Fraga, M. F. & Esteller, M. DNA methylation: a profile of methods and applications. Biotechniques 33, 632–649 (2002).
Pomraning, K. R., Smith, K. M. & Freitag, M. Genome-wide high throughput analysis of DNA methylation in eukaryotes. Methods 47, 142–150 (2009).
Callinan, P. A. & Feinberg, A. P. The emerging science of epigenomics. Hum. Mol. Genet. 15, R95–R101 (2006).
Beck, S. & Rakyan, V. K. The methylome: approaches for global DNA methylation profiling. Trends Genet. 24, 231–237 (2008).
Ushijima, T. Detection and interpretation of altered methylation patterns in cancer cells. Nature Rev. Cancer 5, 223–231 (2005).
Hatada, I. Emerging technologies for genome-wide DNA methylation profiling in cancer. Crit. Rev. Oncog. 12, 205–223 (2006).
Wilson, I. M. et al. Epigenomics: mapping the methylome. Cell Cycle 5, 155–158 (2006).
Lister, R. & Ecker, J. R. Finding the fifth base: genome-wide sequencing of cytosine methylation. Genome Res. 19, 959–966 (2009).
Lieb, J. D. et al. Applying whole-genome studies of epigenetic regulation to study human disease. Cytogenet. Genome Res. 114, 1–15 (2006).
Jacinto, F. V., Ballestar, E. & Esteller, M. Methyl-DNA immunoprecipitation (MeDIP): hunting down the DNA methylome. Biotechniques 44, 35–43 (2008).
Berman, B. P., Weisenberger, D. J. & Laird, P. W. Locking in on the human methylome. Nature Biotech. 27, 341–342 (2009).
Jeddeloh, J. A., Greally, J. M. & Rando, O. J. Reduced-representation methylation mapping. Genome Biol. 9, 231 (2008).
Tompa, R. et al. Genome-wide profiling of DNA methylation reveals transposon targets of CHROMOMETHYLASE3. Curr. Biol. 12, 65–68 (2002).
van der Ploeg, L. H. & Flavell, R. A. DNA methylation in the human γδβ-globin locus in erythroid and nonerythroid tissues. Cell 19, 947–958 (1980).
Waalwijk, C. & Flavell, R. A. DNA methylation at a CCGG sequence in the large intron of the rabbit β-globin gene: tissue-specific variations. Nucleic Acids Res. 5, 4631–4634 (1978).
Kaput, J. & Sneider, T. W. Methylation of somatic vs germ cell DNAs analyzed by restriction endonuclease digestions. Nucleic Acids Res. 7, 2303–2322 (1979).
Gautier, F., Bunemann, H. & Grotjahn, L. Analysis of calf-thymus satellite DNA: evidence for specific methylation of cytosine in C-G. sequences. Eur. J. Biochem. 80, 175–183 (1977).
Liang, G., Gonzalgo, M. L., Salem, C. & Jones, P. A. Identification of DNA methylation differences during tumorigenesis by methylation-sensitive arbitrarily primed polymerase chain reaction. Methods 27, 150–155 (2002).
Frigola, J., Ribas, M., Risques, R. A. & Peinado, M. A. Methylome profiling of cancer cells by amplification of inter-methylated sites (AIMS). Nucleic Acids Res. 30, e28 (2002).
Estecio, M. R. et al. High-throughput methylation profiling by MCA coupled to CpG island microarray. Genome Res. 17, 1529–1536 (2007).
Toyota, M. et al. Identification of differentially methylated sequences in colorectal cancer by methylated CpG island amplification. Cancer Res. 59, 2307–2312 (1999).
Chung, W. et al. Identification of novel tumor markers in prostate, colon and breast cancer by unbiased methylation profiling. PLoS ONE 3, e2079 (2008).
Omura, N. et al. Genome-wide profiling of methylated promoters in pancreatic adenocarcinoma. Cancer Biol. Ther. 7, 1146–1156 (2008).
Shen, L. et al. Integrated genetic and epigenetic analysis identifies three different subclasses of colon cancer. Proc. Natl Acad. Sci. USA 104, 18654–18659 (2007).
Yan, P. S., Potter, D., Deatherage, D. E., Huang, T. H. & Lin, S. Differential methylation hybridization: profiling DNA methylation with a high-density CpG island microarray. Methods Mol. Biol. 507, 89–106 (2009).
Cross, S. H., Charlton, J. A., Nan, X. & Bird, A. P. Purification of CpG islands using a methylated DNA binding column. Nature Genet. 6, 236–244 (1994). The first demonstration of affinity enrichment of methylated DNA.
Tran, R. K. et al. DNA methylation profiling identifies CG methylation clusters in Arabidopsis genes. Curr. Biol. 15, 154–159 (2005).
Pietrobono, R. et al. Quantitative analysis of DNA demethylation and transcriptional reactivation of the FMR1 gene in fragile X cells treated with 5- azadeoxycytidine. Nucleic Acids Res. 30, 3278–3285 (2002).
Nouzova, M. et al. Epigenomic changes during leukemia cell differentiation: analysis of histone acetylation and cytosine methylation using CpG island microarrays. J. Pharmacol. Exp. Ther. 311, 968–981 (2004).
Ordway, J. M. et al. Identification of novel high-frequency DNA methylation changes in breast cancer. PLoS ONE 2, e1314 (2007).
Ordway, J. M. et al. Comprehensive DNA methylation profiling in a human cancer genome identifies novel epigenetic targets. Carcinogenesis 27, 2409–2423 (2006).
Irizarry, R. A. et al. Comprehensive high-throughput arrays for relative methylation (CHARM). Genome Res. 18, 780–790 (2008).
Ibrahim, A. E. et al. MMASS: an optimized array-based method for assessing CpG island methylation. Nucleic Acids Res. 34, e136 (2006).
Schumacher, A. et al. Microarray-based DNA methylation profiling: technology and applications. Nucleic Acids Res. 34, 528–542 (2006).
Rollins, R. A. et al. Large-scale structure of genomic methylation patterns. Genome Res. 16, 157–163 (2006).
Khulan, B. et al. Comparative isoschizomer profiling of cytosine methylation: the HELP assay. Genome Res. 16, 1046–1055 (2006).
Oda, M. et al. High-resolution genome-wide cytosine methylation profiling with simultaneous copy number analysis and optimization for limited cell numbers. Nucleic Acids Res. 37, 3829–3839 (2009).
Brunner, A. L. et al. Distinct DNA methylation patterns characterize differentiated human embryonic stem cells and developing human fetal liver. Genome Res. 19, 1044–1056 (2009).
Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).
Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).
Mikkelsen, T. S. et al. Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448, 553–560 (2007).
Guccione, E. et al. Methylation of histone H3R2 by PRMT6 and H3K4 by an MLL complex are mutually exclusive. Nature 449, 933–937 (2007).
Guenther, M. G., Levine, S. S., Boyer, L. A., Jaenisch, R. & Young, R. A. A chromatin landmark and transcription initiation at most promoters in human cells. Cell 130, 77–88 (2007).
Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nature Methods 4, 651–657 (2007).
Mukhopadhyay, R. et al. The binding sites for the chromatin insulator protein CTCF map to DNA methylation-free domains genome-wide. Genome Res. 14, 1594–1602 (2004).
Weber, M. et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nature Genet. 39, 457–466 (2007).
Weber, M. et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nature Genet. 37, 853–862 (2005).
Keshet, I. et al. Evidence for an instructive mechanism of de novo methylation in cancer cells. Nature Genet. 38, 149–153 (2006). References 76–78 provided the first genome-wide analyses of mammalian genomes using affinity enrichment of methylated DNA.
Farthing, C. R. et al. Global mapping of DNA methylation in mouse promoters reveals epigenetic reprogramming of pluripotency genes. PLoS Genet. 4, e1000116 (2008).
Fouse, S. D. et al. Promoter CpG methylation contributes to ES cell gene regulation in parallel with Oct4/Nanog, PcG complex, and histone H3 K4/K27 trimethylation. Cell Stem Cell 2, 160–169 (2008).
Dindot, S. V., Person, R., Strivens, M., Garcia, R. & Beaudet, A. L. Epigenetic profiling at mouse imprinted gene clusters reveals novel epigenetic and genetic features at differentially methylated regions. Genome Res. 19, 1374–1383 (2009).
Hayashi, H. et al. High-resolution mapping of DNA methylation in human genome using oligonucleotide tiling array. Hum. Genet. 120, 701–711 (2007).
Cheng, A. S. et al. Epithelial progeny of estrogen-exposed breast progenitor cells display a cancer-like methylome. Cancer Res. 68, 1786–1796 (2008).
Gal-Yam, E. N. et al. Frequent switching of Polycomb repressive marks and DNA hypermethylation in the PC3 prostate cancer cell line. Proc. Natl Acad. Sci. USA 105, 12979–12984 (2008).
Smith, A. E. et al. Epigenetics of human T cells during the G0→G1 transition. Genome Res. 19, 1325–1337 (2009).
Koga, Y. et al. Genome-wide screen of promoter methylation identifies novel markers in melanoma. Genome Res. 19, 1462–1470 (2009).
Straussman, R. et al. Developmental programming of CpG island methylation profiles in the human genome. Nature Struct. Mol. Biol. 16, 564–571 (2009).
Down, T. A. et al. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nature Biotech. 26, 779–785 (2008).
Gebhard, C. et al. Rapid and sensitive detection of CpG-methylation using methyl-binding (MB)-PCR. Nucleic Acids Res. 34, e82 (2006).
Gebhard, C. et al. Genome-wide profiling of CpG methylation identifies novel targets of aberrant hypermethylation in myeloid leukemia. Cancer Res. 66, 6118–6128 (2006).
Schmidl, C. et al. Lineage-specific DNA methylation in T cells correlates with histone methylation and enhancer activity. Genome Res. 19, 1165–1174 (2009).
Jorgensen, H. F., Adie, K., Chaubert, P. & Bird, A. P. Engineering a high-affinity methyl-CpG-binding protein. Nucleic Acids Res. 34, e96 (2006).
Rauch, T. & Pfeifer, G. P. Methylated-CpG island recovery assay: a new technique for the rapid detection of methylated-CpG islands in cancer. Lab. Invest. 85, 1172–1180 (2005).
Rauch, T. A. & Pfeifer, G. P. The MIRA method for DNA methylation analysis. Methods Mol. Biol. 507, 65–75 (2009).
Rauch, T. A. et al. High-resolution mapping of DNA hypermethylation and hypomethylation in lung cancer. Proc. Natl Acad. Sci. USA 105, 252–257 (2008).
Ballestar, E. et al. Methyl-CpG binding proteins identify novel sites of epigenetic inactivation in human cancer. EMBO J. 22, 6335–6345 (2003).
Hayatsu, H. Discovery of bisulfite-mediated cytosine conversion to uracil, the key reaction for DNA methylation analysis — a personal account. Proc. Jpn Acad. Ser. B Phys. Biol. Sci. 84, 321–330 (2008).
Wang, R. Y., Gehrke, C. W. & Ehrlich, M. Comparison of bisulfite modification of 5-methyldeoxycytidine and deoxycytidine residues. Nucleic Acids Res. 8, 4777–4790 (1980).
Frommer, M. et al. 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 (1992). Although differential deamination of methylated and unmethylated cytosine residues had been described previously, this study provided a practical demonstration of the technique for the analysis of DNA methylation at the single-base-pair level using PCR amplification.
Clark, S. J., Harrison, J., Paul, C. L. & Frommer, M. High sensitivity mapping of methylated cytosines. Nucleic Acids Res. 22, 2990–2997 (1994).
Paul, C. L. & Clark, S. J. Cytosine methylation: quantitation by automated genomic sequencing and GENESCAN analysis. Biotechniques 21, 126–133 (1996).
Eckhardt, F. et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nature Genet. 38, 1378–1385 (2006). The first example of 'brute force' bisulphite Sanger sequencing of many targets in mammalian genomes.
Adorjan, P. et al. Tumour class prediction and discovery by microarray-based DNA methylation analysis. Nucleic Acids Res. 30, e21 (2002).
Gitan, R. S., Shi, H., Chen, C. M., Yan, P. S. & Huang, T. H. Methylation-specific oligonucleotide microarray: a new potential for high-throughput methylation analysis. Genome Res. 12, 158–164 (2002).
Reinders, J. et al. Genome-wide, high-resolution DNA methylation profiling using bisulfite-mediated cytosine conversion. Genome Res. 18, 469–476 (2008).
Bibikova, M. et al. High-throughput DNA methylation profiling using universal bead arrays. Genome Res. 16, 383–393 (2006).
Bibikova, M. et al. Human embryonic stem cells have a unique epigenetic signature. Genome Res. 16, 1075–1083 (2006).
Bibikova, M. & Fan, J. B. GoldenGate assay for DNA methylation profiling. Methods Mol. Biol. 507, 149–163 (2009).
Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).
Byun, H. M. et al. Epigenetic profiling of somatic tissues from human autopsy specimens identifies tissue- and individual-specific DNA methylation patterns. Hum. Mol. Genet. 18, 4808–4817 (2009).
Ladd-Acosta, C. et al. DNA methylation signatures within the human brain. Am. J. Hum. Genet. 81, 1304–1315 (2007).
Katari, S. et al. DNA methylation and gene expression differences in children conceived in vitro or in vivo. Hum. Mol. Genet. 18, 3769–3778 (2009).
Martinez, R. et al. A microarray-based DNA methylation study of glioblastoma multiforme. Epigenetics 4, 255–264 (2009).
Christensen, B. C. et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 5, e1000602 (2009).
Houseman, E. A. et al. Model-based clustering of DNA methylation array data: a recursive- partitioning algorithm for high-dimensional data arising as a mixture of β distributions. BMC Bioinformatics 9, 365 (2008).
Hinoue, T. et al. Analysis of the association between CIMP and BRAFV600E in colorectal cancer by DNA methylation profiling. PLoS ONE 4, e8357 (2009).
Bibikova, M. et al. Genome-wide DNA methylation profiling using Infinium assay. Epigenomics 1, 177–200 (2009).
Steemers, F. J. & Gunderson, K. L. Whole genome genotyping technologies on the BeadArray platform. Biotechnol. J. 2, 41–49 (2007).
Korshunova, Y. et al. Massively parallel bisulphite pyrosequencing reveals the molecular complexity of breast cancer-associated cytosine-methylation patterns obtained from tissue and serum DNA. Genome Res. 18, 19–29 (2008).
Taylor, K. H. et al. Ultradeep bisulfite sequencing analysis of DNA methylation patterns in multiple gene promoters by 454 sequencing. Cancer Res. 67, 8511–8518 (2007).
Meissner, A. et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868–5877 (2005).
Hodges, E. et al. High definition profiling of mammalian DNA methylation by array capture and single molecule bisulfite sequencing. Genome Res.6 Jul 2009 (doi: 10.1101/gr.095190.109).
Li, J. B. et al. Multiplex padlock targeted sequencing reveals human hypermutable CpG variations. Genome Res. 19, 1606–1615 (2009).
Dunn, J. J., McCorkle, S. R., Everett, L. & Anderson, C. W. Paired-end genomic signature tags: a method for the functional analysis of genomes and epigenomes. Genet. Eng. (NY) 28, 159–173 (2007).
Dempsey, M. P. et al. Paired-end sequence mapping detects extensive genomic rearrangement and translocation during divergence of Francisella tularensis subsp. tularensis and Francisella tularensis subsp. holarctica populations. J. Bacteriol. 188, 5904–5914 (2006).
Korbel, J. O. et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420–426 (2007).
Tost, J., Schatz, P., Schuster, M., Berlin, K. & Gut, I. G. Analysis and accurate quantification of CpG methylation by MALDI mass spectrometry. Nucleic Acids Res. 31, e50 (2003).
Ehrich, M. et al. Cytosine methylation profiling of cancer cell lines. Proc. Natl Acad. Sci. USA 105, 4844–4849 (2008).
Ehrich, M. et al. Quantitative high-throughput analysis of DNA methylation patterns by base-specific cleavage and mass spectrometry. Proc. Natl Acad. Sci. USA 102, 15785–15790 (2005).
Docherty, S. J., Davis, O. S., Haworth, C. M., Plomin, R. & Mill, J. Bisulfite-based epityping on pooled genomic DNA provides an accurate estimate of average group DNA methylation. Epigenetics Chromatin 2, 3 (2009).
Killian, J. K. et al. Large-scale profiling of archival lymph nodes reveals pervasive remodeling of the follicular lymphoma methylome. Cancer Res. 69, 758–764 (2009).
Tetzner, R. Prevention of PCR cross-contamination by UNG treatment of bisulfite-treated DNA. Methods Mol. Biol. 507, 357–370 (2009).
Tetzner, R., Dietrich, D. & Distler, J. Control of carry-over contamination for PCR-based DNA methylation quantification using bisulfite treated DNA. Nucleic Acids Res. 35, e4 (2007).
Dohm, J. C., Lottaz, C., Borodina, T. & Himmelbauer, H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 36, e105 (2008).
Warnecke, P. M. et al. Detection and measurement of PCR bias in quantitative methylation analysis of bisulphite-treated DNA. Nucleic Acids Res. 25, 4422–4426 (1997).
Campan, M., Weisenberger, D. J., Trinh, B. & Laird, P. W. MethyLight. Methods Mol. Biol. 507, 325–337 (2009).
Weisenberger, D. J. et al. Analysis of repetitive element DNA methylation by MethyLight. Nucleic Acids Res. 33, 6823–6836 (2005).
Kerkel, K. et al. Genomic surveys by methylation-sensitive SNP analysis identify sequence-dependent allele-specific DNA methylation. Nature Genet. 40, 904–908 (2008).
Houseman, E. A. et al. Copy number variation has little impact on bead-array-based measures of DNA methylation. Bioinformatics 25, 1999–2005 (2009).
Siegmund, K. D., Marjoram, P., Woo, Y. J., Tavare, S. & Shibata, D. Inferring clonal expansion and cancer stem cell dynamics from DNA methylation patterns in colorectal cancers. Proc. Natl Acad. Sci. USA 106, 4828–4833 (2009).
Fatemi, M. et al. Footprinting of mammalian promoters: use of a CpG DNA methyltransferase revealing nucleosome positions at a single molecule level. Nucleic Acids Res. 33, e176 (2005).
Weisenberger, D. J. et al. DNA methylation analysis by digital bisulfite genomic sequencing and digital MethyLight. Nucleic Acids Res. 36, 4689–4698 (2008).
Li, M. et al. Sensitive digital quantification of DNA methylation in clinical samples. Nature Biotech. 27, 858–863 (2009).
Chhibber, A. & Schroeder, B. G. Single-molecule polymerase chain reaction reduces bias: application to DNA methylation analysis by bisulfite sequencing. Anal. Biochem. 377, 46–54 (2008).
Bock, C. & Lengauer, T. Computational epigenetics. Bioinformatics 24, 1–10 (2008).
Pennisi, E. Research funding. Are epigeneticists ready for big science? Science 319, 1177 (2008).
Jones, P. A. et al. Moving AHEAD with an international human epigenome project. Nature 454, 711–715 (2008).
Pushkarev, D., Neff, N. F. & Quake, S. R. Single-molecule sequencing of an individual human genome. Nature Biotech. 27, 847–852 (2009).
Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).
Branton, D. et al. The potential and challenges of nanopore sequencing. Nature Biotech. 26, 1146–1153 (2008).
Clarke, J. et al. Continuous base identification for single-molecule nanopore DNA sequencing. Nature Nanotechnol. 4, 265–270 (2009).
Model, F., Adorjan, P., Olek, A. & Piepenbrock, C. Feature selection for DNA methylation based cancer classification. Bioinformatics 17, S157–S164 (2001).
Rohde, C. et al. Bisulfite sequencing Data Presentation and Compilation (BDPC) web server — a useful tool for DNA methylation analysis. Nucleic Acids Res. 36, e34 (2008).
Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinformatics 10, 232 (2009).
Xu, Y. H., Manoharan, H. T. & Pitot, H. C. CpG Analyzer, a Windows-based utility program for investigation of DNA methylation. Biotechniques 39, 656–662 (2005).
Hackenberg, M. et al. CpGcluster: a distance-based algorithm for CpG-island detection. BMC Bioinformatics 7, 446 (2006).
Wang, Y. & Leung, F. C. An evaluation of new criteria for CpG islands in the human genome as gene markers. Bioinformatics 20, 1170–1177 (2004).
Takai, D. & Jones, P. A. The CpG Island Searcher: a new WWW resource. In Silico Biol. 3, 235–240 (2003).
Xu, Y. H., Manoharan, H. T. & Pitot, H. C. CpG PatternFinder: a Windows-based utility program for easy and rapid identification of the CpG methylation status of DNA. Biotechniques 43, 334–342 (2007).
Ioshikhes, I. P. & Zhang, M. Q. Large-scale human promoter mapping using CpG islands. Nature Genet. 26, 61–63 (2000).
Carr, I. M., Valleley, E. M., Cordery, S. F., Markham, A. F. & Bonthron, D. T. Sequence analysis and editing for bisulphite genomic sequencing projects. Nucleic Acids Res. 35, e79 (2007).
Hetzl, J., Foerster, A. M., Raidl, G. & Mittelsten Scheid, O. CyMATE: a new tool for methylation analysis of plant genomic DNA after bisulphite sequencing. Plant J. 51, 526–536 (2007).
Pelizzola, M. et al. MEDME: an experimental and analytical methodology for the estimation of DNA methylation levels based on microarray derived MeDIP-enrichment. Genome Res. 18, 1652–1659 (2008).
Pattyn, F. et al. methBLAST and methPrimerDB: web-tools for PCR based methylation analysis. BMC Bioinformatics 7, 496 (2006).
Grunau, C., Renault, E., Rosenthal, A. & Roizes, G. MethDB — a public database for DNA methylation data. Nucleic Acids Res. 29, 270–274 (2001).
Grunau, C., Renault, E. & Roizes, G. DNA Methylation Database 'MethDB': a user guide. J. Nutr. 132, 2435S–2439S (2002).
Amoreira, C., Hindermann, W. & Grunau, C. An improved version of the DNA methylation database (MethDB). Nucleic Acids Res. 31, 75–77 (2003).
Negre, V. & Grunau, C. The MethDB DAS server: adding an epigenetic information layer to the human genome. Epigenetics 1, 101–105 (2006).
Li, L. C. & Dahiya, R. MethPrimer: designing primers for methylation PCRs. Bioinformatics 18, 1427–1431 (2002).
Grunau, C., Schattevoy, R., Mache, N. & Rosenthal, A. MethTools — a toolbox to visualize and analyze DNA methylation data. Nucleic Acids Res. 28, 1053–1058 (2000).
He, X. et al. MethyCancer: the database of human DNA methylation and cancer. Nucleic Acids Res. 36, D836–D841 (2008).
Rakyan, V. K. et al. An integrated resource for genome-wide identification and analysis of human tissue-specific differentially methylated regions (tDMRs). Genome Res. 18, 1518–1529 (2008).
Ongenaert, M. et al. PubMeth: a cancer methylation database combining text-mining and expert annotation. Nucleic Acids Res. 36, D842–D846 (2008).
Kumaki, Y., Oda, M. & Okano, M. QUMA: quantification tool for methylation analysis. Nucleic Acids Res. 36, W170–W175 (2008).
Acknowledgements
I am grateful to K. Siegmund and to members of the University of Southern California Epigenome Center for many helpful discussions. P.W.L.'s research is supported by National Cancer Institute grants R01-CA118699 and U24-CA143882 and by the Norris Foundation, the Ovarian Cancer Research Fund, the Canary Foundation, the Entertainment Industry Foundation and the Riley Foundation.
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Peter W. Laird is consultant for Epigenomics, AG, which has a commercial interest in DNA methylation biomarkers.
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Glossary
- Transposons
-
Mobile DNA elements that can relocate within the genome of their hosts.
- Restriction–modification system
-
A set of enzymes found in many bacteria and archaea that protects the host genome from genomic parasites. Restriction–modification systems consist of sequence-specific restriction endonucleases, which target invading DNA, and associated DNA methyltransferases with similar recognition sequences, which protect the host genome from the action of the endonucleases.
- Mismatch repair
-
A DNA-repair pathway that removes mismatched bases and corrects the insertion or deletion of short stretches of (repeated) DNA.
- CpG islands
-
In eukaryotic genomes, regions of several hundred base pairs that are not depleted of CpGs by 5-methylcytosine deamination owing to them being unmethylated in the germ line. They often overlap transcription start sites. Most definitions of CpG islands set a minimum length (for example, 200 or 500 bp), a minimum observed:expected CpG ratio (for example, greater than 0.6 or 0.65) and a minimum GC content (for example, 50% or 55%).
- Isoschizomers
-
Pairs of structurally distinct restriction enzymes with the same recognition sequence and the same cleavage positions.
- Neoschizomers
-
Pairs of structurally distinct restriction enzymes with the same recognition sequence but with different cleavage positions.
- Imprinted
-
A locus with monoallelic expression determined by the parental origin of the allele.
- Chromatin immunoprecipitation
-
A technique that is used to identify the location of DNA-binding proteins and epigenetic marks in the genome. Genomic sequences containing the mark of interest are enriched by binding soluble DNA chromatin extracts (complexes of DNA and protein) to an antibody that recognizes the mark. Related techniques — such as methylated DNA immunoprecipitation — use antibodies to recognize DNA modifications directly.
- Array capture
-
A method for enriching whole genomic DNA for many regions of interest by hybridization to an array containing RNA or DNA sequences complementary to the regions of interest.
- Padlock capture
-
A method for simultaneously capturing and amplifying large numbers of regions of interest from whole genomic DNA. Each padlock probe has two complementary oligonucleotide sequences that flank a region of interest. The sequences are joined by a loop of DNA that ensures efficient joint hybridization and contains sequences for PCR with universal primers.
- Solution hybrid selection
-
A method for enriching whole genomic DNA for many regions of interest by hybridization to a complex library of RNA or DNA sequences in solution, followed by retrieval of the annealed hybrids.
- Hemimethylated
-
Methylation of a residue on one strand within a palindromic target sequence but not of the corresponding residue within the palindromic target sequence on the complementary DNA strand. Not be confused with monoallelic methylation, in which one allele of a locus is methylated in a diploid organism.
- β distribution
-
A continuous probability distribution with an interval between 0 and 1. Two positive parameters, α and β, are used to define β distributions.
- Median absolute deviation
-
A measure of statistical dispersion that is less influenced by outliers and extreme values than standard deviation. It is defined as the median of the collection of absolute deviations from the data set's median.
- Quantile normalization
-
A method for equalizing the total signal intensities and distributions of probe signal strengths among arrays or among colour channels on an array. It sorts all probes by signal strength and then matches probes at each rank position among arrays and forces the values at each rank position to be equal. An identical distribution of probe signal strengths among the arrays or colour channels is obtained.
- LOESS normalization
-
A computationally intensive method in which a polynomial regression is fitted to each point in the data and more weight is given to data nearer the point of interest. It is often applied to hybridization array data to remove differences in global signal intensity among data sets or colour channels.
- MA plot
-
A representation of microarray data in which M (vertical axis) is the intensity ratio between the red (R) and green (G) colour channels (M=log(R/G)) and A (horizontal axis) is the mean intensity (A=(logR+logG)/2). This representation is often used as a basis for normalizing microarray data, with the underlying assumptions that dye bias is dependent on signal intensity, that the majority of probes do not have very different signal intensities among channels and that approximately the same number of probes in each channel have signal intensities that are stronger than the equivalent probes in the other channel.
- Targeted indexing
-
Indexing refers to the incorporation of short sequences as tagged codes during the construction of a sequencing library, followed by the simultaneous parallel sequencing of libraries from many sources. The source of the DNA sequence for each read can be deduced from the index. This technique can be combined with targeted sequencing of regions of interest enriched by hybrid selection.
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Laird, P. Principles and challenges of genome-wide DNA methylation analysis. Nat Rev Genet 11, 191–203 (2010). https://doi.org/10.1038/nrg2732
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DOI: https://doi.org/10.1038/nrg2732
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