Epigenetic modifications such as carbon 5 methylation of the cytosine base in a CpG dinucleotide context are involved in the onset and progression of human diseases. A comprehensive understanding of the role of genome-wide DNA methylation patterns, the methylome, requires quantitative determination of the methylation states of all CpG sites in a genome. So far, analyses of the complete methylome by whole-genome bisulfite sequencing (WGBS) are rare because of the required large DNA quantities, substantial bioinformatic resources and high sequencing costs. Here we describe a detailed protocol for tagmentation-based WGBS (T-WGBS) and demonstrate its reliability in comparison with conventional WGBS. In T-WGBS, a hyperactive Tn5 transposase fragments the DNA and appends sequencing adapters in a single step. T-WGBS requires not more than 20 ng of input DNA; hence, the protocol allows the comprehensive methylome analysis of limited amounts of DNA isolated from precious biological specimens. The T-WGBS library preparation takes 2 d.
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Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).
Kobayashi, H. et al. Contribution of intragenic DNA methylation in mouse gametic DNA methylomes to establish oocyte-specific heritable marks. PLoS Genet. 8, e1002440 (2012).
Popp, C. et al. Genome-wide erasure of DNA methylation in mouse primordial germ cells is affected by AID deficiency. Nature 463, 1101–1105 (2010).
Seisenberger, S. et al. The dynamics of genome-wide DNA methylation reprogramming in mouse primordial germ cells. Mol. Cell 48, 849–862 (2012).
Gu, H. et al. Preparation of reduced representation bisulfite sequencing libraries for genome-scale DNA methylation profiling. Nat. Protoc. 6, 468–481 (2011).
Khulan, B. et al. Comparative isoschizomer profiling of cytosine methylation: the HELP assay. Genome Res. 16, 1046–1055 (2006).
Meissner, A. et al. Reduced representation bisulfite sequencing for comparative high-resolution DNA methylation analysis. Nucleic Acids Res. 33, 5868–5877 (2005).
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).
Miura, F., Enomoto, Y., Dairiki, R. & Ito, T. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res. 40, e136 (2012).
Adey, A. & Shendure, J. Ultra-low-input, tagmentation-based whole-genome bisulfite sequencing. Genome Res. 22, 1139–1143 (2012).
Adey, A. et al. Rapid, low-input, low-bias construction of shotgun fragment libraries by high-density in vitro transposition. Genome Biol. 11, R119 (2010).
Quail, M.A. et al. A large genome center's improvements to the Illumina sequencing system. Nat. Methods 5, 1005–1010 (2008).
Weber, M. et al. Chromosome-wide and promoter-specific analyses identify sites of differential DNA methylation in normal and transformed human cells. Nat. Genet. 37, 853–862 (2005).
Taiwo, O. et al. Methylome analysis using MeDIP-seq with low DNA concentrations. Nat. Protoc. 7, 617–636 (2012).
Brinkman, A.B. et al. Whole-genome DNA methylation profiling using MethylCap-seq. Methods 52, 232–236 (2010).
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).
Bock, C. et al. Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat. Biotechnol. 28, 1106–1114 (2010).
Harris, R.A. et al. Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications. Nat. Biotechnol. 28, 1097–1105 (2010).
Hovestadt, V. et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol. 125, 913–916 (2013).
Sturm, D. et al. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell 22, 425–437 (2012).
Grunenwald, H.L, Caruccio, N., Jendrisak, J. & Dahl, G. Transposon end compositions and methods for modifying nucleic acids. US patent 20100120098A1 (2010).
Hansen, K.D., Langmead, B. & Irizarry, R.A. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biol. 13, R83 (2012).
We gratefully acknowledge excellent technical support by M. Helf and helpful discussions with D. Lipka. We also acknowledge the excellent support by the sequencing core facility at DKFZ. Work in the Plass laboratory was supported by the Helmholtz Foundation and the German Federal Ministry of Education and Science in the program for medical genome research (FKZ; no. 01KU1001A). Q.W. obtained support by the Humboldt Research Fellowship for Postdoctoral Researchers. A.A. is funded by a National Science Foundation Graduate Research Fellowship.
A provisional US patent application has been deposited for aspects of these methods (A.A., J.S.).
Integrated supplementary information
(a) Prior to oligo replacement/gap repair, the DNA has a size range of about 700–10,000 bp. The non-tagmented DNA (not shown) was ≥ 20 kb in size. (b) Three sequencing libraries prepared from the DNA shown in a without bisulfite treatment. FU, fluorescence units.
Supplementary Figure 2 PCR amplification curves and size distributions of T-WGBS libraries prepared from six different human blood samples.
Input DNA amount was 20 ng each. (a) PCR amplification curves indicate that all 6 samples are close to the plateau phase at cycle 12. (b) Size distributions of the six T-WGBS libraries ranging from about 200 bp to 600 bp with size peaks around 300 bp. The high spikes flanking the curves represent size markers. FU, fluorescence units.
(a,b) Consistency in base composition of sequencing reads between T-WGBS (a) and conventional WGBS (b). The base composition bias in the first bases of the T-WGBS reads is caused by the Tn5 transposase which has a slight base preference at certain positions of the integration target sequence (please see the paragraph entitled ‘Limitations’ in the INTRODUCTION for further information).
Supplementary Figure 4 Scatter plot versions of the comparative methylation levels shown in Figure 4a,b.
(a) High consistency of methylation level estimates between T-WGBS and conventional WGBS at single CpGs covered at least 30-fold (r = 0.95). (b) High reproducibility of T-WGBS indicated by the similarity of the methylation levels (r = 0.92) in windows of 5 CpGs (read numbers too low for single CpG analysis) in libraries from two independent tagmentations analyzed on a single HiSeq2000 lane each.
Supplementary Figure 5 High consistency, 97.8% and 98.3%, between T-WGBS and conventional WGBS methylation data from two human blood samples, M and K, respectively.
For each sample, two lanes of T-WGBS data and three lanes of conventional WGBS were compared. (a,b) Methylation levels were calculated based on scanning windows of 5 CpGs with at least 30-fold coverage and displayed on density plots (a) and scatter plots (b). The dotted lines mark the 0.2 difference between the x and y axes. Consistency values were calculated by subtracting from 100% the respective numbers in the corners of the density plots displayed in (a); these numbers indicate the percentage of data points that fall above/below the envelope marked with dotted lines. The r values in (b) indicate Pearson correlation.
(a) 98.5% consistency between T-WGBS and conventional WGBS. (b) 98.1% consistency is in an analogous analysis as in (a) between the two sequencing strands, designated Watson and Crick, from the conventional WGBS. The lower figures show scatter plot versions of the figures at the top. Consistency defined as less than 0.2 (20%) difference in methylation level in windows of 5 CpGs. Consistency values were calculated by subtracting from 100% the respective numbers in the corners of the upper two density plots; these numbers indicate the percentage of data points that fall above/below the envelope marked with dotted lines (marking the 0.2 difference between the x and y axes). The r-values indicate Pearson correlation.
Supplementary Figure 7 Comparison of sequencing coverage of cytosines in CpG, CHG and CHH context (H can be A, C or T) between T-WGBS and conventional WGBS.
Coverage versus cytosine density in 1-kb windows for WGBS (red) and T-WGBS (blue). The coverage appears stable in genomic regions with 100–400 cytosines, likely because the majority of 1-kb regions has a cytosine density between 10% and 40%. Variation becomes larger in the two extremes of the cytosine density for both T-WGBS and conventional WGBS due to low counts in each category.
Supplementary Figure 8 Comparison of sequencing coverage in relation to the GC content in 200-bp windows between T-WGBS and conventional WGBS.
Coverage versus local GC content in 200-bp windows, roughly the median length of genomic DNA in the library fragments, for WGBS (red) and T-WGBS (blue). Overall, T-WGBS has a higher coverage along a wide range of GC content.
Sequencing data alignment and methylation calling methods (PDF 84 kb)
Comparison of read numbers, duplications, coverage, methylation level and conversion between T-WGBS and conventional WGBS. (XLS 29 kb)
Comparison of CpG coverage between conventional WGBS and T-WGBS. (XLS 22 kb)
Control of size distribution of three DNA samples after tagmentation. (PDF 176 kb)
PCR amplification curves and size distributions of T-WGBS libraries prepared from six different human blood samples. (PDF 74 kb)
Base composition. (PDF 117 kb)
Scatter plot versions of the comparative methylation levels shown in Figure 4a,b. (PDF 321 kb)
High consistency, 97.8% and 98.3%, between T-WGBS and conventional WGBS methylation data from two human blood samples, M and K, respectively. (PDF 255 kb)
High consistency of methylation levels between T-WGBS and conventional WGBS. (PDF 456 kb)
Comparison of sequencing coverage of cytosines in CpG, CHG and CHH context (H can be A, C or T) between T-WGBS and conventional WGBS. (PDF 79 kb)
Comparison of sequencing coverage in relation to the GC content in 200-bp windows between T-WGBS and conventional WGBS. (PDF 47 kb)
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Wang, Q., Gu, L., Adey, A. et al. Tagmentation-based whole-genome bisulfite sequencing. Nat Protoc 8, 2022–2032 (2013). https://doi.org/10.1038/nprot.2013.118
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