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NEBNext® Enzymatic Methyl-seq (EM-seq™)
A high-performance alternative to bisulfite sequencing for methylome analysis
Introduction
DNA methylation analysis, and specificallythe identification of 5-methylcytosine (5mC)
and 5-hydroxymethylcytosine (5hmC) withingenomes, is important as these modifications are
known to affect expression of genes. In general,
low levels of methylation near transcription startsites are associated with higher transcription
levels, while genes with high levels of cytosine
modification in regulatory regions are expressedat lower levels. Complete and accurate
methylome analysis is important in many fields:
including the study of disease states such as
cancer, in monitoring embryonic development,
and in studies of agricultural plants. However,existing technologies for methylome analysis
have significant drawbacks.
Whole Genome Bisulfite Sequencing (WGBS)
has long been the gold standard for methylome
analysis, but the chemical bisulfite reaction
damages and degrades DNA, resulting infragmentation and loss. Additionally, bisulfite
libraries demonstrate significant GC bias and areenriched for methylated regions.

FIGURE 1: The NEBNext
Enzymatic Methyl-seq(EM-seq) workflow

The highly effective enzymatic conversion inthis method minimizes damage to DNA and,with the supplied NEBNext Ultra™ II library
preparation workflow reagents, produces highquality libraries that enable superior detection of
5mC and 5hmC from fewer sequencing reads.
Conveniently, the EM-seq method results in thesame converted sequence as WGBS and so thesame analysis pipelines can be used.

End Repair/dA-Tailing

DNA is end-repaired
and dA-tailed

EM-seqAdaptor Ligation

DNA is ligated to the
EM-seq adaptors

Oxidation of
5mC and 5hmC

TET2 and Oxidation Enhancer protect
5mC/5hmC from deamination

Deaminationof C to U

APOBEC deaminates cytosines touracils; oxidized forms of 5mC/
5hmC are not deaminated

PCR
Amplification

Library amplification using
NEBNext Q5U Master Mix andNEBNext index primers

Sequencing

Sequencing on the Illumina® platform

NEBNext® Ultra™ II
reagents

Input is 10–200 ng of genomic
DNA, sheared to 300 bp

FIGURE 2: EM-seq and sodium
bisulfite conversion methods
Sodium bisulfite
methodhm

EM-seqmethodm

C C G T C G G AC C G C

hmm

C C G T C G G AC C G C

TET2/Oxidation
Enhancer
ca/g

ca/g

C C G T C G G AC C G CAPOBEC

Workflow
In the EM-seq workflow (Figure 1), as withWGBS libraries, the first step is library
construction from sheared DNA. For EM-seq,
standard input amounts can range from
10-200 ng of sheared DNA, and a modified
protocol is also available for input amounts ashigh as 500 ng. This is followed by two setsof enzymatic conversion steps to differentiate
unmethylated cytosines from 5mC and
5hmC. Finally, libraries are PCR amplified
before sequencing.
The two-step enzymatic conversion of EM-seq
is shown in more detail in Figure 2, alongsidebisulfite conversion.

Genomic DNA
Shearing

04/19

To overcome these limitations, we developed an
enzyme-based approach, NEBNext Enzymatic
Methyl-seq (EM-seq), a new method for
identification of 5mC and 5hmC.

Sodium bisulfite treatment of DNA results in thedeamination of cytosines to uracils, while themodified forms of cytosine (5mC and 5hmC) are
not deaminated. When bisulfite treated DNA is
PCR amplified, uracils are replaced by thymines,
and 5mC and 5hmC are replaced by cytosines.
Once sequenced, unmethylated cytosines are
represented by thymines and 5mC and 5hmC
are represented by cytosines. By comparing
sequences to reference sequences (C/T and G/Aconverted genome), the methylation status canbe assessed.

hmm

U U G T C G G AU U G C

ca/g

ca/g

U U G T C G G AU U G C

Converted
T T G T C G G AT T G C

T T G T C G G AT T G C

Sequenced

The first EM-seq conversion step uses TET2
and an Oxidation Enhancer to protect modified
cytosines from downstream deamination.
TET2 enzymatically oxidizes 5mC and5hmC through a cascade reaction into
5-carboxycytosine [5-methylcytosine (5mC)→ 5-hydroxymethylcytosine (5hmC) →5-formylcytosine (5fC) → 5-carboxycytosine(5caC)]. This protects 5mC and 5hmC fromdeamination. 5hmC can also be protected fromdeamination by glucosylation to form 5ghmc
using the Oxidation Enhancer.
The second enzymatic step uses APOBEC,
which deaminates cytosine but does not affect
5caC and 5ghmC.
The resulting converted sequence is the sameas that for bisulfite-treated DNA and so can beanalyzed in the same way. Typical aligners used
to analyze data include, but are not limited to,
Bismark and bwa-meth.

1

Larger library insert sizes

FIGURE 3: EM-seq libraries have larger insert sizes
50 ng Human NA12878 genomic DNA was sheared to 300 bp using the Covaris® S2 instrument and used as input into EM-seq
and WGBS protocols. For WGBS, NEBNext Ultra II DNA was used for library construction, followed by the Zymo Research EZ DNA
Methylation-Gold™ kit for bisulfite conversion. Libraries were sequenced on an Illumina® MiSeq® (2 x 76 bases) and insert sizes
were determined using Picard 2.18.14. The normalized frequency of each insert size was plotted, illustrating that library insert sizes
are larger for EM-seq than for WGBS, and indicating that EM-seq does not damage DNA as bisulfite treatment does in WGBS.
14,000EM-seq
WGBS

12,00010,000Count

The more gentle treatment of DNA by thesteps in the EM-seq workflow, comparedto the harsh bisulfite treatment, minimizes
damage to DNA. As a result, EM-seq converted
DNA is more intact than bisulfite converted
DNA, resulting in libraries with a higher
percentage of longer inserts, as shown in
Figure 3. This enables longer sequencing reads,
resulting in greater confidence in mapping,and potentially lower per-base sequencing
costs depending on instrumentation and othersequencing reaction details.

8,000
6,000
4,000
2,000
0

0

50

100

150

200

250

300

350

400

Insert Size (bp)Increased library yields

10, 50 and 200 ng Human NA12878 genomic DNA was sheared
to 300 bp using the Covaris S2 instrument and used as input into
EM-seq and WGBS protocols. For WGBS, NEBNext Ultra II DNA
was used for library construction, followed by the Zymo Research
EZ DNA Methylation-Gold Kit for bisulfite conversion. For all input
amounts, EM-seq library yields were higher, and fewer PCR cycles
were required, suggesting greater DNA loss in the WGBS protocol.
Error bars indicate standard deviation.

80
70

EM-seqFIGURE 4B: EM-seq resultsin lower duplication rates
10, 50 and 200 ng Human NA12878 genomic DNA was sheared
to 300 bp using the Covaris S2 instrument and used as input into
EM-seq and WGBS protocols. For WGBS, NEBNext Ultra II DNA
was used for library construction, followed by the Zymo Research
EZ DNA Methylation-Gold Kit for bisulfite conversion.
Libraries were sequenced on an Illumina NovaSeq 6000 (2 x
100 bases), and reads were aligned to hg38 using bwa-meth0.2.2. Duplication rates were determined using MarkDuplicates
tool. Duplication levels in EM-seq libraries are lower than WGBS
libraries, which is also consistent with the lower number of PCR
cycles required for EM-seq libraries.

WGBS

60

60

50

50

Duplication (%)

Importantly, these higher yields are not due
to the presence of PCR duplicates, which canbe especially apparent at low input amounts.
Indeed, EM-seq libraries display consistently
low levels of duplicates across a range of input
amounts (Figure 4B).

FIGURE 4A: EM-seq produceshigher yields

Library Yield (nM)

The DNA damage, fragmentation and loss that
result from bisulfite treatment reduce yields ofbisulfite-converted libraries after amplification.
In contrast, the more gentle treatment in the
EM-seq workflow allows maintenance of highquality DNA libraries, which can be efficiently
amplified. As a result, EM-seq library yields
are not only higher than WGBS library yields,
but are achieved with fewer PCR cycles(Figure 4A).

40
30
20
10

WGBS

40
30
20
10

0

DNA input
PCR cycles

EM-seq

10 ng
8
10

50 ng
6
8

200 ng
4
6

0

10

50
DNA Input (ng)2002Global methylation levels
While in mammals, 5mC and 5hmC arefound predominantly in the CpG context, inplants 5mC also occurs in CHG and CHH
contexts, where H = A, C or T. To assess
cytosine methylation detection accuracy,
global methylation levels in CpG, CHG andCHH contexts were determined for humanDNA. These were found to be similar between
EM-seq and WGBS in each context (Figure 5).

FIGURE 5: Total methylation detected by EM-seq and WGBS
is similar
10, 50 and 200 ng Human NA12878 genomic DNA was sheared to 300 bp using the Covaris S2 instrument and used as input into
EM-seq and WGBS protocols. For WGBS, NEBNext Ultra II DNA was used for library construction, followed by the Zymo Research EZ
DNA Methylation-Gold Kit for bisulfite conversion. Libraries were sequenced on an Illumina NovaSeq 6000 (2 x 100 bases).
324 million paired end reads for each library were aligned to hg38 using bwa-meth 0.2.2. and methylation information was extracted from thealignments using MethylDackel. Methylation levels for NA12878 are similar between EM-seq and WGBS in CpG, CHH and CHG contexts.
CpG

60

CHG

CHH
EM-seq
WGBS

50

Methylation (%)

40

30

20

10

0

Uniformity of GC coverage

Since bisulfite treatment acts upon anddamages unmethylated cytosines, which
comprise the majority of cytosines, this harshtreatment therefore disproportionately affects
GC-containing regions. This context-specificdamage, breakage and loss lead to bisulfitetreated libraries being under-represented for
GC content and over-represented for AT. In
contrast, EM-seq libraries show uniform GC
coverage, highlighting the lack of damage toDNA, and that the libraries are representativeof the original sample. (Figure 6).

50

200

10

50
DNA Input (ng)

200

10

50

200

FIGURE 6: EM-seq has superior uniformity of GC coverage
10, 50 and 200 ng Human NA12878 genomic DNA was sheared to 300 bp using the Covaris S2 instrument
and used as input into EM-seq and WGBS protocols. For WGBS, NEBNext Ultra II DNA was used for library construction,
followed by the Zymo Research EZ DNA Methylation-Gold Kit for bisulfite conversion. Libraries were sequenced on an
Illumina NovaSeq® 6000 (2 x 100 bases). Reads were aligned to hg38 using bwa-meth 0.2.2. GC coverage was analyzed
using Picard 2.18.14 and the distribution of normalized coverage across different GC contents of the genome (0-100%)
was plotted. EM-seq libraries have significantly more uniform GC coverage, and lack the AT over-representation and GC
under-representation typical of WGBS libraries.

3.5
3

Normalized Coverage

While sufficient yield of a library is required
for successful sequencing, the quality of alibrary is also critical. A high-quality librarywill have uniform representation of theoriginal sample, including uniform coverage
across the GC spectrum.

10

EM-seq 10 ng

WGBS 10 ng

EM-seq 50 ng

WGBS 50 ng

EM-seq 200 ng

WGBS 200 ng

2.5
2
1.5
1
0.5
0

0

10

20

30

40

50

60

70

80

90

100

GC Content (%)

3

Increased CpG coveragewith EM-seq

Analysis of the number of CpGs detected byeach method, at different levels of stringency,
allows a comparison of the sensitivity of CpG
detection by EM-seq and WGBS. Figure 8
demonstrates that EM-seq libraries identified
more CpGs than bisulfite libraries using
the same number of reads (324 million) for
10 ng, 50 ng and 200 ng inputs, and EM-seq
libraries show a higher percentage of CpGs
covered, at the lower minimum coverage
depths. For example, when the number of
unique and common CpGs identified by
EM-seq and WGBS libraries at 1X and 8X
minimum coverage for a range of input levels
is measured, EM-seq covers at least 20% more
CpGs at 1X minimum coverage thresholdcompared to WGBS. This difference in CpG
coverage increases to two-fold at 8X minimum
coverage threshold.

0.1

Variance in Dinucleotide Coverage

Examination of dinucleotide coverage
distribution of libraries shows the variancein coverage for dinucleotides in sequencereads when compared to unconverted
library dinucleotide distribution. This wasplotted across all 16 possible dinucleotide
combinations, for EM-seq and WGBS.
While the EM-seq libraries show uniform
coverage across all dinucleotide combinations,
WGBS libraries are depleted in C-containing
dinucleotides, and are enriched in A/T
containing dinucleotides (Figure 7).

Reads were aligned to hg38 using bwa-meth 0.2.2. Dinucleotide coverage distribution was plotted for all 16 possiblecombinations, comparing variance in coverage in the reads for EM-seq or WGBS with unconverted Ultra II library reads.
EM-seq libraries show uniformity in coverage for all dinucleotides, while WGBS libraries are depleted in C-containing
dinucleotides and enriched in A/T containing dinucleotides.

0EM-seq
10 ng
50 ng
200 ng

WGBS
10 ng
50 ng
200 ng

-0.1
-0.2
-0.3
-0.4
-0.5
-0.6

AC

TC

CA

CT

CC

CG

GC

GG

CN or NC

AG

GA

TG

GT

GN or NG

TT

TA

AT

AA

A or T

FIGURE 8: EM-seq identifies more CpGs than WGBS,
at lower sequencing coverage depth
Reads were aligned to hg38 using bwa-meth 0.2.2. Coverage of CpGs with EM-seq and WGBS libraries wasanalyzed using 324 million paired end reads. Each top and bottom strand CpGs were counted independently,
yielding a maximum of 56 million possible CpG sites. EM-seq identifies more CpGs at lower depth of sequencing.
60

CpGs Covered (Millions)

In depth analysis of sequencing data allows
exploration of uniformity of genome coverage
and representation of CpGs.

FIGURE 7: EM-seq libraries show uniformity in coverage
for all dinucleotides

50

EM-seq 10 ng

WGBS 10 ng

EM-seq 50 ng

WGBS 50 ng

EM-seq 200 ng

WGBS 200 ng

40
30
20
10
0

1

6

11

16

Observed Coverage Depth

21

26

4

Plotting the number of unique and common
CpGs detected by EM-seq and WGBS
using the same number of sequence reads,
for different DNA inputs and at different
stringency levels, also highlights that EM-seq
enables detection of more CpGs, and thisdifference is more striking at higher stringency(Figure 9). At 1X coverage, while EM-seq
identified ~54 million CpGs using 10, 50 and
200 ng input DNA, WGBS identified ~36 to
46 million. Using 8X read coverage threshold,the number of CpGs identified using EM-seq
ranged from 11 to 16.6 million, comparedto 1.6 to 7.1 million for WGBS. This data
demonstrates that more relevant data (CpG
coverage) can be achieved with fewer sequencereads using EM-seq compared to WGBS.

FIGURE 9: EM-seq identifies more CpGs than WGBS, at lower
sequencing coverage depth
10, 50 and 200 ng Human NA12878 genomic DNA was sheared to 300 bp using the Covaris S2 instrument and used as input into
EM-seq and WGBS protocols. For WGBS, NEBNext Ultra II DNA was used for library construction, followed by the Zymo Research EZ DNA
Methylation-Gold Kit for bisulfite conversion. Libraries were sequenced on an Illumina NovaSeq 6000 (2 x 100 bases). Reads were aligned
to hg38 using bwa-meth 0.2.2. Coverage of CpGs with EM-seq and WGBS libraries was analyzed using 324 million paired-end reads.
The number of unique and common CpGs identified by EM-seq and WGBS at 1X and 8x minimum coverage for each input amount are
shown. EM-seq covers at least 20% more CpGs than WGBS at 1X minimum coverage threshold. The difference in CpG coverage increases
to two-fold at 8X minimum coverage threshold.
1X minimum
coverage

17.9M

35.8M

0.2M 9.8M

44.4M

0.2M

9.5M

44.6M

0.2M

8X minimum
coverage
10.3M

12.5M

0.7M 0.9M

10 ng

4.1M 2.5M

11 M

50 ng

4.3 M 2.8 M

200 ngUnique EM-seq

Unique WGBS

CommonDistribution of CpGs across genomic features
Identification of genomic features should be consistent across the range of input amounts, and thisis true for EM-seq (Figure 10A). Comparison of identification of these features, at stringent (8X)
minimum coverage, shows that both EM-seq and WGBS identify a wide range of genomic features,
including repeat elements and transcriptional regulatory regions. However, significantly more uniqueloci are consistently found in EM-seq at that coverage level (Figure 10B and C).
FIGURE 10: EM-seq identifies more genomic features than WGBS, across a range
of input amounts
324 million paired reads were analyzed for each library and annotated using Homer. The number of genomic features identified is similar across a broad input range,
indicating no loss in the power of CpG detection even with decreasing DNA input (A). EM-seq and WGBS identify a wide range of genomic features at 8X minimum
coverage (B & C). These include repeat elements and transcriptional regulatory regions, with common loci identified within each genomic feature. However, moreunique loci are found exclusively in EM-seq libraries at 8X minimum coverage threshold.
A.

B.

Satellite
SimplerepeatLTR
Lowcomplexity

C.

LTR
Promoter

SimplerepeatIntergenic

Low
complexity

IntronCpG island

Exon

5´-UTR

SINE

TTS

LINE

3´-UTRCpG island
5´-UTR

Satellite

PromoterIntergenic
Intron
Exon
SINE
LINE

0

TTS

200 ng3´-UTR

10 ng

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0

25

50

75 100 125 150 175 200

Thousands of CpGs

Millions of CpGs

50 ng

0

2

4

6

8

10

12

Unique EM-seq

Unique WGBS

Common14

Millions of CpGs

5

CpG coverage and consistency
Correlations between libraries from different input amounts and different methods is a useful measureof consistency as well as performance efficiency. Figure 11 shows CpG methylation correlations
between EM-seq and WGBS libraries, from a range of input amounts, examining 21 million CpGs
common to all 12 libraries shown. EM-seq correlations were high between all input amounts of
EM-seq libraries, indicating that methylation detection sensitivity does not decrease with input amount.
In contrast, correlations between WGBS libraries were the lowest. Correlations between EM-seq
and WGBS libraries were highest at higher input amounts. Taken together, this data highlights therobustness of EM-seq methylation profiles.
FIGURE 11: EM-seq libraries have higher CpG correlations than WGBS libraries

10 ng (1)0.8

10 ng (2)0.8

50 ng (1)0.8

50 ng (2)0.8

200 ng (1)0.8

200 ng (2)0.8

10 ng (1)0.8

10 ng (2)0.8

50 ng (1)0.8

50 ng (2)0.8

200 ng (1)0.8

200 ng (2)WGBS

EM-seqCorrelations between 10, 50 and 200 ng EM-seq and WGBS libraries were plotted using methylKit with 1X minimum coverage (21 million CpGs common to all libraries).
Correlations were highest between all inputs of the EM-seq libraries, with Pearson’s correlation ranging from 0.82 to 0.86. Correlations between WGBS libraries ranged from0.71 to 0.8. Comparisons between EM-seq and WGBS libraries had highest correlations with 50 ng and 200 ng WGBS inputs while the lowest correlations were observed with
10 ng WGBS DNA inputs. This data reveals that methylation profiles from EM-seq libraries are more robust, when compared both within EM-seq and across WGBS datasets.

0.8

0.82

0.4

0.84

0.84

0.84

0.84

0.76

0.75

0.80

0.80

0.81

0.80

0.84

0.84

0.84

0.84

0.76

0.75

0.80

0.80

0.80

0.80

0.85

0.85

0.86

0.78

0.76

0.81

0.82

0.82

0.82

0.85

0.85

0.78

0.76

0.81

0.82

0.82

0.82

0.85

0.78

0.76

0.81

0.82

0.82

0.82

0.78

0.76

0.81

0.82

0.82

0.82

0.71

0.76

0.76

0.76

0.76

0.74

0.75

0.75

0.75

0.79

0.80

0.80

0.80

0.80

0

0.4
0

0.4
0

0.4
0

0.4
0

0.4
0

0.4
0

0.4
0

0.4
0

0.4
0

0.80

0.4
0

0.4
0

0

0.4 0.8

10 ng (1)0

0.4 0.8

10 ng (2)0

0.4 0.8

50 ng (1)0

0.4 0.8

50 ng (2)EM-seq0

0.4 0.8

200 ng (1)0

0.4 0.8

200 ng (2)0

0.4 0.8

10 ng (1)0

0.4 0.8

10 ng (2)0

0.4 0.8

50 ng (1)0

0.4 0.8

50 ng (2)0

0.4 0.8

200 ng (1)0

0.4 0.8

200 ng (2)

WGBS

6

The distribution of CpG coverage for EM-seq and WGBS is shown in Figure 12. While for WGBS,
a high percentage of CpGs are covered at a lower level, especially for low input amounts, for
EM-seq the majority of CpGs are covered at a higher level, and there is a tighter distribution ofcoverage level.

FIGURE 12: More CpGs are covered using EM-seq than WGBSThe read coverage per base for 10, 50 and 200 ng EM-seq and WGBS libraries was plotted using methylKit. The percentage of CpGs within a
specific bin is displayed at the top of each bar. The histogram for EM-seq is shifted right for EM-seq compared to WGBS, showing that more CpGs
are detected at higher coverage for EM-seq libraries than with WGBS, at all input amounts.

44.3

10 ng

28.3

15
10

0

200 ng

25

41.4

40.7

20

11.5
7.6

5

50 ng

25

Frequency (Millions)

Frequency (Millions)

25

4.5

15
10
8.3

5

3.5

1

0

2

6.2

5

3.1
0.1

0

36.4

20

Frequency (Millions)A. EM-seq

Read Coverage per Base (log10)

35.5

15
10
8.7

5
3.3

0.2

1

0

20

0

2

Read Coverage per Base (log10)

5.7

5.3

0.2

0

1

2

Read Coverage per Base (log10)

B. WGBS

23.1
17.7
13.9

5

6.3
1.8

0

0

1

0.4

2

Read Coverage per Base (log10)

The difference in coverage level distribution
is further exemplified in the violin plots in
Figure 13, which shows substantially higher
median CpG coverage for EM-seq compared
to WGBS, at all input amounts.

20.7

21.4

14.4

5

9.8
4.3

0

0

200 ng

15

26.5

10

2.3

0.5 0.1

1

2

Read Coverage per Base (log10)

25

10

19.6

21.6

13.9
10.8

5

5.1

0

0

3

0.8 0.1

1

2

Read Coverage per Base (log10)

FIGURE 13: EM-seq CpG coverage is higher
than WGBS across a range of input amounts
Distribution of CpG coverage is displayed in violin plots and the median, 90th and 10th
quantile of the observed CpG coverage for the distribution are represented. EM-seq data for
all DNA inputs is similar, with a median CpG overage of around 11-13x. WGBS libraries had
significantly lower median coverage (~4x), that decreased to around 2X for 10 ng inputs.

EM-seq

WGBS

100

80

Coverage (%)

Frequency (Millions)

Frequency (Millions)

10

50 ng

15

Frequency (Millions)

10 ng

15 36.8

60

40

20

0

10 ng

50 ng

200 ng7

Transcriptional control features

FIGURE 14: Enhanced representation of transcriptional control featuresusing EM-seq
The lower C-containing dinucleotide representation associated with WGBS results in the potential loss of methylated and non-methylated CpG
data around important transcription control elements. Transcriptional activity is controlled by the methylation status of these elements andaccurately defining transcriptional control elements is therefore important.
Heatmaps generated by deepTools show the distribution of coverage in a 2 kb window around transcription start sites (TSS) (A), and a 1kb
window around CTCF transcriptional repressor protein binding site (B), CpG islands (C) and H3K27me3 histone methylation site (D).
In all cases, EM-seq libraries have greater, and more uniform coverage than WGBS, and the enhanced coverage demonstrated using EM-seq
results in fewer spurious methylation calls.
A. TSS

B. CTCF
10 ng

EM-seq

200 ng

50 ng

WGBS

EM-seqWGBS

EM-seq

10 ng

WGBS

40
20
0

EM-seq

200 ng

50 ng

WGBS

EM-seqWGBS

EM-seq

WGBS

60
40
20
0

Coverage60

Coverage

80

80

70

70

60

60

50

50

40

40

30

30

20
20
10
10

Gene Distance

End

+1 kb

Start

End

+1 kb
-1 kb

Start

End

+1 kb
-1 kb

Start

End

Start

Distance from CTCF

C. CpG islands

D. H3K27me3

10 ng
EM-seq

+1 kb
-1 kb

End

Start

+1 kb
-1 kb

End

+1 kb
-1 kb

Start

-1 kb

TSS

+2 kb

TSS

+2 kb
-2 kb

TSS

+2 kb
-2 kb

TSS

+2 kb
-2 kb

TSS

+2 kb
-2 kb

TSS

+2 kb
-2 kb

-2 kb

0

200 ng

50 ng

WGBS

EM-seqWGBS

EM-seq

10 ng

WGBS

EM-seq20
0

EM-seqWGBS

EM-seq

WGBS
60
40
20
0

Coverage40

200 ng

50 ng

WGBS

Coverage

60

80

70

70
60
60
50
50
40
40
30
30
20
20
10

Distance from CpG Island

+1 kb

End

Start

+1 kb
-1 kb

End

Start

End

Start

+1 kb
-1 kb

End

Start

+1 kb
-1 kb

End

+1 kb
-1 kb

Start

+1 kb
-1 kb

End

Start

-1 kb

End

+1 kb

Start

End

Start

+1 kb
-1 kb

End

Start

+1 kb
-1 kb

End

+1 kb
-1 kb

Start

End

+1 kb
-1 kb

Start

End

+1 kb
-1 kb

Start

10

-1 kb

In expressed genes, the regions at transcription
start sites are generally unmethylated, whilegenes that are expressed at lower levels
generally have high levels of cytosine
methylation in regulatory regions. It istherefore important to be able to accurately
define transcriptional control elements.
However, since, as described above, WGBS
data traditionally has lower C-containing
dinucleotide representation, this results in thepotential loss of CpG data around importanttranscription control elements. The distributionof coverage 1-2kb around several importanttranscriptional elements was examined, for
EM-seq and WGBS: Transcription start sites(TSS) (A), CTCF transcriptional repressor
protein binding site (B), CpG islands (C) andH3K27me3 histone methylation site (D). The
more complete landscape, provided by EM-seq
methodology, around these important sites, due
to both the greater levels of coverage overall,and the greater uniformity of coverage, enable
more confident analysis.

Distance from H3K27me3

8

Examination of CpG coverage and methylation around transcription start sites is shown in Figure 15.
With EM-seq, coverage of CpGs is both significantly higher than WGBS in these 4kb windows, andalso strikingly uniform, lacking the dip in coverage close to the transcription start site (TSS) itself
characteristic of WGBS. The information on methylation garnered from EM-seq at 8X stringency alsoaligns more closely with expected methylation patterns, with a reduction in methylation levels close
to the TSS.
FIGURE 15: EM-seq is superior to WGBS for detection of CpG methylation
around transcription start sites
Methylation of CpGs at each input was determined around the transcription start site (TSS). The 4 kb window around TSS was divided into 400 10 bp
bins, and CpGs within these 10 bp bins with 8X or higher coverage were used to plot methylation. A: EM-seq has higher and more uniform coverage
across TSSs . B: The average percentage methylation of 8X covered CpGs for EM-seq and WGBS libraries is shown. The EM-seq data is more representative of the expected methylation pattern across TSSs, with lowest levels at the TSS, and increasing methylation at the +/- 2 kb extremes.

EM-seqBins with
CpGs Covered (%)

A. Coverage across transcription start sites
100

10 ng

50 ng

100

100

80

80

80

60

60

60

40

40

40

20

20

20

0
-2 kb

0
-2 kb

TSS

70

50 ng

TSS

+2 kb

0
-2 kb

+2 kb

WGBS

200 ngTSS+2 kb

B. Average percent methylation of 8X-covered CpGs

60

50

50

50

40

40

40

30

30

30

20

20

20

10
-2 kb

10
-2 kb

TSS

+2 kb

TSS

Methylome analysis
in Arabidopsis

10
-2 kb

+2 kb

TSS

+2 kb

FIGURE 16: EM-seq accurately represents 5mC levels within the
Arabidopsis thaliana genome
50 ng A. thaliana genomic DNA was sheared to 300 bp using the Covaris S2 instrument and used as input into EM-seq and WGBS protocols.
For WGBS, NEBNext Ultra II DNA was used for library construction, followed by the Zymo Research EZ DNA Methylation-Gold Kit for bisulfite
conversion. Libraries were sequenced on an Illumina NextSeq® 500 (2 x 75 bases). 125 million paired end reads for each library were aligned
to TAIR10 using bwa-meth 0.2.2 and methylation information was extracted from the alignments using MethylDackel.
A. Total 5mC levels were compared between LCMS (Liquid Chromatography Mass Spectrometry) and sequencing data from EM-seq and WGBS
libraries. EM-seq levels are close to the 5mC levels determined using LCMS. B and C. CpG and CHN methylation in Arabidopsis using EM-seq
and WGBS. This data taken together indicate that WGBS suffers from over-estimation of methylated cytosines compared to EM-seq.

A. TotalB. CpG

C. CHN

12

40

10

10

5mC/Total C (%)

In plants, cytosine methylation is present in
the context not only of CpG but also of CHG
and CHH, where H = A, C or T. Total levelsof 5mC in Arabidopsis thaliana were determined
by LCMS (Liquid Chromatography-Mass
Spectrometry), WGBS and EM-seq. LCMS
data are closer to EM-seq than WGBS, andmethylation percentages were also higher for
WGBS for CpG and CHN contexts, suggesting
over-estimation of methylated cytosines.

200 ng

70

60

8
6
4
2
0

LCMS EM-seq WGBS

5mC in CHN Context (%)

10 ng

60

5mC in CpG Context (%)

Average percent
methylation

70

30

20

10

0

EM-seq

WGBS

8
6
4
2
0

EM-seqWGBS

9Sensitivity of methylation detection in Arabidopsis thaliana was also compared between EM-seq and
WGBS, and CpG, CHH and CHG sites were identified and counted. Figure 16 demonstrates that
EM-seq libraries cover more CpG, CHH and CHG sites at higher stringency than WGBS.
FIGURE 17: With Arabidopsis thaliana, EM-seq identifies more CpGs than
WGBS, at lower sequencing coverage depth
50 ng A. thaliana genomic DNA was sheared to 300 bp using the Covaris S2 instrument and used as input into EM-seq and WGBS protocols.
For WGBS, NEBNext Ultra II DNA was used for library construction, followed by the Zymo Research EZ DNA Methylation-Gold Kit for bisulfite
conversion. Libraries were sequenced on an Illumina NextSeq 500 (2 x 75 bases). 125 million paired end reads for each library were aligned to
TAIR10 using bwa-meth 0.2.2. CpG, CHH and CHG sites on both strands were counted independently. EM-seq identifies more CpGs, CHHs andCHGs, at higher coverage depth compared to WGBS, resulting in more usable information.

35

5

30

4
3
2
1

25
20
15
10
5

1

11

21

31

41

51

Observed Coverage Depth

EM-seq

7

WGBS

6
5
4
3
2
1
0

0

0

C. CHG

CHGs Covered (Millions)

B. CHH

6

CHHs Covered (Millions)

CpGs Covered (Millions)A. CpG

1

11

21

31

41

1

51

Observed Coverage Depth

11

21

31

41

51

Observed Coverage Depth

Conclusion
NEBNext Enzymatic Methyl-seq (EM-seq) is a new method for identification of 5mC and 5hmC. The
enzyme-based conversion in the NEBNext Enzymatic Methyl-seq Kit minimizes damage to DNA and,with the supplied NEBNext Ultra II library preparation workflow reagents, produces high quality
libraries that enable superior detection of 5mC and 5hmC from fewer sequencing reads.
• Superior sensitivity of detection of 5mC and 5hmC
• Greater mapping efficiency• More uniform GC coverage• Detection of more CpGs with fewer sequence reads• Uniform dinucleotide distribution• High-efficiency library preparation, with larger library insert sizes• Utility with a range of sample types and input amounts

Ordering Information
PRODUCT

NEB #

SIZE

NEBNext Enzymatic Methyl-seq Kit

E7120S/L

24/96 rxns

NEBNext Enzymatic Methyl-seq Conversion Module

E7125S/L

24/96 rxns

NEBNext Multiplex Oligos for Enzymatic Methyl-seq (Unique Dual Index Primer Pairs)

E7140S/L

24/96 rxns

NEBNext Q5U Master Mix

M0597S/L

50/250 rxns™

One or more of these products are covered by patents, trademarks and/or copyrights owned or controlled by New England Biolabs, Inc. For more information,please email us at gbd@neb.com. The use of these products may require you to obtain additional third party intellectual property rights for certain applications.
Your purchase, acceptance, and/or payment of and for NEB’s products is pursuant to NEB’s Terms of Sale at www.neb.com/support/terms-of-sale. NEB does not agree
to and is not bound by any other terms or conditions, unless those terms and conditions have been expressly agreed to in writing by a duly authorized officer of NEB.
COVARIS® is a registered trademark of Covaris, Inc. ILLUMINA®, MISEQ®, NEXTSEQ® and NOVASEQ® are registered trademarks of Illumina, Inc.
EZ DNA METHYLATION™ is a trademark of Zymo Research, Corp.
© Copyright 2019, New England Biolabs, Inc.; all rights reserved.

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