Methylation in Arabidopsis exists in three sequence contexts, CG, CHG (where H = A, C, or T), and asymmetric CHH ¹. We observed overall genome-wide levels of 24% CG, 6.7% CHG, and 1.7% CHH methylation (Supplementary Fig. 1a). Most CGs were either unmethylated or were highly methylated (80–100%), whereas CHH sites were either unmethylated or methylated at ~10%. CHG sites showed a more uniform distribution between 20–100% (Supplementary Fig. 1b-d). These differences underscore the fact that each type of methylation is under distinct genetic control ¹. Our reads also contained 504-fold average coverage of 99.97% of theoretically-coverable cytosines in the unmethylated chloroplast genome ^{3, 8}, giving false positive rates of 0.29% (CG), 0.29% (CHG), and 0.25% (CHH) (Supplementary Fig. 1a, Supplementary Fig. 2). The BS-Seq data were highly consistent with traditional bisulfite sequencing data from individual methylated or unmethylated loci ³ (Supplementary Table 2, Supplementary Fig. 3, and below).

While CG, CHG, and CHH methylation were highly correlated, showing enrichment in repeat-rich pericentromeric regions (Fig. 1a), a striking deviation was found within gene bodies, which contained almost exclusively CG methylation (Fig. 1b). This is consistent with previous studies ^{3, 4, 9} and with a depletion of short interfering RNAs (siRNAs) in the bodies of genes (Fig. 1b). Conversely, genomic regions corresponding to siRNAs were highly correlated with CG, CHG, and CHH methylation, consistent with the known molecular nature of RNA-directed DNA methylation (Fig. 1c) ¹. For methylation of all types there was a strong positive correlation with the length of the methylated sequence (Fig. 1d).

BS-Seq appears to be more sensitive than previously-employed microarray-based methods ^3-5. For example, we found a cluster of 5 methylated CG sites in a 34 base pair region and a lone methylated CG site, both within the FWA locus, that were not detected by previous methods (Supplementary Fig. 4). We also found CG methylation within genes previously classified as unmethylated ^{3, 4} (Supplementary Fig. 5). Finally, in analyzing genes whose expression is de-repressed in DNA methyltransferase mutants, BS-Seq was more accurate in identifying genes with promoter methylation that was otherwise variably detected in previous microarray studies (Supplementary Fig. 6).

BS-Seq can be used to analyze repetitive sequences that are difficult to study with microarrays as they may exceed the dynamic detection range or cross-hybridize. For example, we mapped methylation across the highly repetitive rDNA loci and found high levels of CG, CHG, and CHH methylation, including on the minimal promoter and upstream Sal1 repeats (Supplementary Fig. 7). Further, we detected methylation in telomeric repeat sequences (CCCTAAA)_n which have not been previously shown to be methylated (Fig. 1e). Interestingly, the vast majority of methylation occurred at the cytosine in the third position (Fig. 1e).

The single base resolution of BS-Seq allows determination of the precise boundaries between methylated and unmethylated regions. For example, we found that the boundary between tandem repeats and flanking DNA showed a sharp drop in methylation, but DNA methylation extended from inverted repeats into flanking DNA, showing a more gradual reduction (Fig. 1b). This apparent “spreading” of methylation was not correlated with siRNA spreading because siRNA abundance levels drop sharply at the flanks of both tandem and inverted repeats (Fig. 1b).

We analyzed the relationship between sequence context and preference of methylation. We calculated the percent methylation of all possible 7-mer sequences in which the methylated cytosine was either in the fifth position (allowing an analysis of four nucleotides upstream of CG, CHG, and CHH methylation; Fig. 2, Supplementary Table 3) or in the first position (allowing analysis of 6 nucleotides following the methylated cytosine; Supplementary Fig. 8, Supplementary Table 4). To ensure that sequence preferences were not simply 7-mers enriched in particular components of the genome, we analyzed either all of chromosome 1, only sequences previously defined to be methylated by methyl-DNA immunoprecipitation, or a group of 9,507 body-methylated genes containing mostly CG methylation ³ (Fig. 2, Supplementary Fig. 8 and 9). We observed a surprisingly high level of sequence context specificity. The highest and lowest methylated 7-mers showed a 13-fold difference for CG-methylation, an 11-fold difference for CHG methylation, and > 900-fold difference for CHH-methylation (Supplementary Table 3). Sequences with the lowest CG methylation were highly enriched for the sequence ACGT (Fig. 2, Supplementary Fig. 9). Poorly methylated CHG sites were depleted of upstream cytosines but tended to contain cytosine following the methylated C. This trend is consistent with nearest-neighbour analysis of wheat germ DNA that found CAG and CTG sites methylated at a higher level than CCG sites ¹⁰. Highly methylated CHH sequences had a very specific configuration, with a tendency for cytosines and CG dinucleotides to be present upstream (Supplementary Table 3) and the sequence TA following the methylated cytosine. In contrast, poorly methylated CHH sequences always contained a cytosine following the methylated cytosine, and frequently contained a cytosine but always lacked an adenine two nucleotides downstream (Fig. 2, Supplementary Fig. 8). These results are consistent with data from individual plant genes showing that cytosines preceding a cytosine are undermethylated while those following a cytosine are more heavily methylated ^11-13, and with asymmetric methylation in mammalian genomes that is found at CT and CA sequences more frequently than CC sequences ¹⁴. It is also of interest that Arabidopsis telomere sequences (CCCTAAA)_n are composed of nearly optimal asymmetric target units, possibly explaining the high methylation of the third cytosines (Fig. 1e). While the molecular basis for these trends is unknown, the results suggest that DNA methyltransferases show strong sequence preferences beyond the CG, CHG, and CHH contexts. Finally, we found that regions with higher concentrations of CG dinucleotides were more heavily methylated at CG sites (Supplementary Fig. 10). Interestingly, this is different from mammalian genomes that show the opposite trend: CGs are depleted in methylated regions and at a higher density in unmethylated CpG islands.

We used autocorrelation analysis to examine the correlation between methylation in different sequence contexts and methylation at adjacent residues. We observed significant correlation between methylated cytosines for distances up to 5,000 nucleotides or more, a likely reflection of regional foci of methylation throughout the genome and of large blocks of pericentromeric heterochromatin (Supplementary Fig. 11, Supplementary Table 5). We also found a high correlation of CHG and CHH methylation within several nucleotides downstream of methylated CG sites, and a tendency for CHH methylation four nucleotides downstream of methylation at CHG sites (Fig. 2, Supplementary Fig. 12, Supplementary Table 5). These data suggest complex interactions between the different types of methylation.

We analyzed the propensity for full methylation of the strand-symmetrical CG and partially symmetrical CHG sequences. As expected, CG methylation on one strand was highly correlated with CG methylation on the opposing strand. We also saw a high correlation for CHG methylation of the two strands, showing that, like CG methylation, CHG sites show a strong tendency for symmetrical methylation (Supplementary Fig. 12). Unexpectedly, we observed a correlation between CHH methylation on one strand, and methylation at the cytosine three nucleotides downstream and on the opposite strand (Supplementary Fig. 12, Supplementary Table 5). Since the sequence of such sites is CHHG, this shows that “asymmetric” methylation shows a propensity for symmetrical methylation at these sites, even though methylation on CHHG sites is not particularly prominent in the genome (Supplementary Fig. 8, Supplementary Table 4).

Autocorrelation analysis also revealed a striking periodicity of 10 nucleotides (the length of one helical DNA turn) for CHH methylation (Fig. 3a, b). We confirmed this period in data from the whole genome and from regions previously defined to be methylated, and confirmed that the periodicity was not due to our computational filtering of the data (Supplementary Fig. 13). We observed this period both when looking at average methylation of cytosines in the genome (Fig. 3a, b, Supplementary Fig. 13) and when individual reads are directly examined (Supplementary Fig. 14). Mammalian Dnmt3a was recently shown to act as a tetramer with Dnmt3L, and two active sites methylate two CG sequences spaced ~8–10 nucleotides apart ¹⁵. Since DRM2 is the main enzyme controlling asymmetric methylation and is the ortholog of Dnmt3a ¹⁶, these data suggest that the mechanism of action of these enzymes may be conserved between plants and mammals.

Autocorrelation also showed a period of 167 nucleotides (Fig. 3c, Supplementary Fig. 15), which is similar to, but slightly shorter than, estimates of the average spacing of nucleosomes in plant chromatin ^17-19. One explanation for this period is that nucleosomes or particular histone modifications might dictate access to the DNA by methyltransferase proteins. Furthermore, the slightly shorter length of 167 nucleotides relative to most estimates of plant nucleosome repeat length (175-185 nt) ^17-19 suggests that DNA methylated chromatin may be more compact because of shorter linker regions or depletion in linker histones ²⁰.

We utilized BS-Seq to study the genome-wide effects of a variety of methyltransferase mutants on DNA methylation (Fig. 4). The MET1, CMT3, and DRM1/DRM2 DNA methyltransferase enzymes are mostly responsible for CG, CHG, and CHH methylation, respectively, though at many loci CHG and CHH methylation is redundantly controlled by CMT3 and DRM1/DRM2 ^{1, 12}. We sequenced and mapped ~90 million nucleotides of BS-Seq data from each of several combinations of DNA methyltransferase mutants (Supplementary Table 1) including met1 single mutants, cmt3 single mutants, drm1 drm2 double mutants, met1 cmt3 double mutants, met1 drm1 drm2 triple mutants, and drm1 drm2 cmt3 triple mutants ²¹. We then analyzed the effect of these mutants on global methylation, the methylation on genes and chromosomes, and the methylation on rDNA and telomeres (Supplementary Table 6, Fig. 1e, Fig. 4, Supplementary Fig. 7, 16). The met1 single mutant, or any mutant combination containing met1, essentially eliminated CG methylation throughout the genome. For instance, gene body methylation, which is almost exclusively CG, was eliminated in all met1-containing strains (Fig. 4a). Surprisingly, in the met1 drm1 drm2 triple mutant, we observed a marked hypermethylation of CHG sites in the bodies of genes (Fig. 4a). This methylation was skewed toward the 3’ end and in this way assumed a pattern of methylation similar to the missing CG methylation. Although previous studies have suggested that the drm1 drm2 cmt3 triple mutant eliminates CHG and CHH methylation ¹², BS-Seq data shows residual methylation (Supplementary Table 6), particularly in pericentromeric heterochromatin (Fig. 4b), suggesting that another enzyme is involved ²². Furthermore, the met1 cmt3 double mutant was equally effective in reducing CHH methylation as was drm1 drm2 cmt3 (Supplementary Table 6), suggesting that CHH methylation depends in part on the presence of CG and CHG methylation. These compensating behaviours suggest that the different DNA methyltransferases act redundantly, and help explain the viability of these mutant combinations whereas the met1 cmt3 drm1 drm2 quadruple mutant causes embryonic lethality ²¹.

The BS-Seq procedure described here should be generally useful in other organisms. For example we applied BS-Seq to quantify the overall genomic methylation difference between wild type mouse embryonic stem cells and cells carrying a mutation in the UHRF1 gene recently shown to control maintenance of CG methylation ^{23, 24}. By analyzing ~60 million nucleotides of shotgun sequencing data from each, we found that Uhrf1−/− cells contained only 25% of the CpG methylation level of wild type (Fig. 4c). Furthermore, to demonstrate that the complete analysis pipeline used for Arabidopsis is applicable to larger genomes, we produced a library from mouse germ cell tissue and generated ~46 million nucleotides of high quality mapped BS-seq data. Approximately 66% of the reads mapped uniquely, a level only slightly lower than that of Arabidopsis (Supplementary Table 1), suggesting that it is practical to apply BS-Seq to entire mammalian genomes.