Overview of the DMRcaller package
Introduction
DNA methylation is an epigenetic modification of the DNA where a methyl group is added to the cytosine nucleotides. This modification is heritable, able to control the gene regulation and, in general, is associated with transcriptional gene silencing. While in mammals the DNA is predominantly methylated in CG context, in plants non-CG methylation (CHG and CHH, where H can be any of the A, C or T nucleotides) is also present and is important for the epigenetic regulation of transcription. Sequencing of bisulfite converted DNA has become the method of choice to determine genome wide methylation distribution. The DMRcaller package computes the set of Differentially Methylated Regions (DMRs) between two samples. DMRcaller will compute the differentially methylated regions from Whole Genome Bisulfite Sequencing (WGBS) or Reduced Representation Bisulfite Sequencing (RRBS) data. There are several tools able to call DMRs, but most work has been done in mammalian systems and, thus, they were designed to primarily call CG methylation.
Methods
The package computes the DMRs using the CX report files generated by Bismark (Krueger & Andrews, 2011), which contain the number of methylated and unmmethylated reads for each cytosine in the genome. The coverage at each position on the genome is not homogeneous and this makes it difficult to compute the differentially methylated cytosines. Here, we implemented three methods:
noise_filter where we use a kernel (Hebestreit et al., 2013) to smooth the number of methylated reads and the total number of reads (the DMRcaller package provides four kernels:
"uniform","triangular","gaussian"and"epanechnicov")neighbourhood where individual cytosines in a specific context are considered in the analysis without any smoothing
bins where the genome is split into equal bins where all the reads are pooled together
The DMRs are then computed by performing a statistical test between
the number of methylated reads and the total number of reads in the two
conditions for each position, cytosine or bin. In particular, we
implemented two statistical tests: \((i)\) Fisher’s exact test and \((ii)\) the Score test. The former (Fisher’s
exact test) uses the fisher.test in the stats
package.
The Score test is a statistical test of a simple null hypothesis that a parameter of interest is equal to some particular value. In our case, we are interested if the methylation levels in the two samples are equal or different. Given that \(m_1\) is the number of methylated reads in condition 1, \(m_2\) is the number of methylated reads in condition 2, \(n_1\) is the total number of reads in condition 1 and \(n_2\) is the total number of reads in condition 2, the Z-score of the Score test is
\[ Z = \frac{(p_1 - p_2)\,\nu}{\sqrt{p(1-p)}} \]
where \(p_1=m_1/n_1\), \(p_2 = m_2/n_2\),
\[ p = \frac{m_1 + m_2}{n_1 + n_2}\quad \textrm{and}\quad \nu = \sqrt{\frac{n_1n_2}{n_1 + n_2}} \]
We then convert the Z-score to the p-value assuming a normal distribution and a two sided test.
Finally, for both statistical tests (Fisher’s exact test and Score test), we adjust the p-values for multiple testing using Benjamini and Hochberg’s method (Benjamini & Hochberg, 1995) to control the false discovery
The algorithm performs the statistical test for each position, cytosine or bin and then marks as DMRs all positions/cytosines/bins that satisfy the following three conditions:
the difference in methylation levels between the two conditions is statistically significant according to the statistical test;
the difference in methylation proportion between the two conditions is higher than a threshold value;
the mean number of reads per cytosine is higher than a threshold.
To group adjacent DMRs, we run an iterative process, where neighbouring DMRs (within a certain distance of each other) are joined only if these three conditions are still met after joining the DMRs.
Finally, we filter the DMRs as follow
Remove DMRs whose lengths are less than a minimum size.
Remove DMRs with fewer cytosines than a threshold value.
For a set of potential DMRs (e.g. genes, transposable elements or CpG
islands) the user can call the function filterDMRs where
all reads in a set of provided regions are pooled together and then the
algorithm performs the statistical test for each region.
Description
Data
Bisulfite sequencing data
Bismark (Krueger
& Andrews, 2011) is a popular tool for methylation call
on WGBS or RRBS data. DMRcaller takes as inputs the CX report
files generated by Bismark and stores this data in a
GRanges object. In the package, we included two CX report
files that contain the methylation calls of WT and met1-3
Arabidopsis thaliana (Stroud et al., 2013).
MET1 gene encodes for the main DNA methyltransferase in
Arabidopsis thaliana and the met1-3 mutation
results in a genome-wide loss of DNA methylation (mainly in CG context).
Due to running time, we restricted the data and analysis to the first
\(1\ Mb\) of the third chromosome of
A. thaliana.
To load a different dataset, one can use readBismark
function, which takes as input the filename of the CX report file to be
loaded.
# specify the Bismark CX report files
saveBismark(methylationDataList[["WT"]],
"chr3test_a_thaliana_wt.CX_report")
saveBismark(methylationDataList[["met1-3"]],
"chr3test_a_thaliana_met13.CX_report")
# load the data
methylationDataWT <- readBismark("chr3test_a_thaliana_wt.CX_report")
methylationDataMet13 <- readBismark("chr3test_a_thaliana_met13.CX_report")
methylationDataList <- GRangesList("WT" = methylationDataWT,
"met1-3" = methylationDataMet13)methylationDataList is a GRangesList object
, where the GRanges elements contain four metadata
columns
context - the context of the Cytosine (CG, CHG or CHH)
readsM - the number of methylated reads
readsN - the total number of reads
trinucleotide_context - the specific context of the cytosine (H is replaced by the actual nucleotide)
If the data consists of two or more replicates, these can be pooled
together using the function poolMethylationDatasets or
poolTwoMethylationDatasets (in the case of pooling only two
datasets). The latter function (poolTwoMethylationDatasets)
is useful when the datasets are large and creating a
GRangesList object is not possible (e.g. the
GRanges objects are two large).
# load the data
methylationDataAll <- poolMethylationDatasets(methylationDataList)
# In the case of 2 elements, this is equivalent to
methylationDataAll <- poolTwoMethylationDatasets(methylationDataList[[1]],
methylationDataList[[2]])Alternatively, one can use ReadBismarkPool to directly
read a list of CX report files and pool them together.
Oxford Nanopore methylation data
Oxford Nanopore Technologies (ONT) provides direct detection of
modified bases (e.g., 5mC) during sequencing by modelling
electrical current disruptions at DNA pores. The methylation information
is embedded as MM (modified base) and ML
(modification probability) tags in aligned BAM files, which
are typically produced after basecalling and alignment from raw signal
files (.pod5).
To analyse ONT methylation data with DMRcaller, users should first:
Get raw signal data (
.pod5) from ONT sequencing device or public resources (e.g., the 1000 Genomes ONT dataset).Basecall the signals using dorado, a GPU-accelerated basecaller by ONT.
Align the reads and embed
MM/MLtags using dorado with methylation-aware models.Decode the tags and summarise methylation calls with
readONTbam().
Oxford Nanopore cytosine reference selection
To analyse Oxford Nanopore (ONT) methylation data, one must first
define the genomic positions of interest, i.e., the candidate cytosines
in a specific methylation context (CG, CHG, or
CHH). The selectCytosine() function provides
this functionality by scanning a reference genome and returning a
GRanges object of relevant cytosine positions.
library(BSgenome.Hsapiens.UCSC.hg38)
# Select cytosines in CG context on chromosome 1
gr_cpg <- selectCytosine(
genome = BSgenome.Hsapiens.UCSC.hg38,
context = "CG",
chr = "chr1"
)
gr_cpgThe resulting GRanges object contains one genomic range
per cytosine that matches the specified context. For CG
context, each range spans two bases (e.g., CpG). The
following metadata columns are attached:
context - A factor indicating the context (
"CG","CHG", or"CHH").trinucleotide_context - Character or factor giving the surrounding trinucleotide sequence (or
NAfor"CG").
This function is particularly useful when preparing to quantify
methylation signals from ONT BAM files using the
readONTbam() function, as it provides the input cytosine
sites to be queried in the aligned sequencing data.
Reading Oxford Nanopore BAM files
After alignment and basecalling, you can extract methylation calls
using the readONTbam() function. This function decodes
MM/ML tags and calculates per-site read-level
methylation counts across the genome
# Path to ONT BAM (with MM/ML tags)
bamfile <- system.file("extdata",
"scanBamChr1Random5.bam",
package="DMRcaller")
# Decode methylation and compute read-level counts
OntChr1CG <- readONTbam(
bamfile = bamfile,
ref_gr = gr_cpg,
genome = BSgenome.Hsapiens.UCSC.hg38,
modif = "C+m?",
chr = "chr1",
region = FALSE,
prob_thresh= 0.5,
parallel = FALSE,
)
# View results
OntChr1CGNotes on important parameters
genome: The genome argument is required ifregion = FALSE, and must be aBSgenomeobject from the Bioconductor package BSgenome. This allows the function to extract cytosine coordinates and sequence contexts from the full reference genome. For example:
modif: The modif parameter specifies which base modifications to extract based on theBAMfile’sMMtag. The syntax follows the SAMtags specification and is usually one of “C+m?”, “C+m.”, “C+h?” or “C+h.”
For example, in this package we provide methylation data derived from
the GM18501 and GM18876 B-Lymphocyte cell
lines from the 1000 Genomes ONT dataset (Gustafson et al., 2024):
- Raw data: GM18501, GM18876
- Basecalling: performed using
dorado v0.9.6with the modeldna_r10.4.1_e8.2_400bps_hac@v5.2.0
The resulting BAM files contain MM and ML
tags for 5mC detection. These are processed with
readONTbam() to compute per-cytosine methylation
statistics. The example data included in the package is accessible
as:
This is a GRangesList object with one
GRanges per sample (GM18501 and
GM18876), covering \(1.5–2
Mbp\) of human chromosome 1. Each GRanges object
contains the following metadata columns:
context - the context of the Cytosine (
CG,CHGorCHH)readsM - the number of methylated reads
readsN - the total number of reads
trinucleotide_context - the specific context of the cytosine (
His replaced by the actual nucleotide)ONT_Cm - comma-delimited read-indices called modified
ONT_C - comma-delimited read-indices covering but unmodified
This function allows for fine-grained downstream analysis, such as identifying differentially methylated regions (DMRs), variably methylated domains (VMDs), or co-methylation structures across samples.
Low resolution profiles
The DMRcaller package also offers the possibility to
visualise context specific global changes in the methylation profile. To
achieve this, the user can call
plotMethylationProfileFromData function, which computes the
mean methylation proportion in tiling bins of fixed size; see Figure
@ref(fig:lowResProfile).
plotMethylationProfileFromData(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
conditionsNames = c("WT","met1-3"),
windowSize = 10000,
autoscale = FALSE,
context = c("CG"))## Recompute regions...
## Computing low resolution profiles...
## Calculating methylation profile for Chr3:101..999999 using a window of 10000 bp
## Calculating methylation profile for Chr3:101..999999 using a window of 10000 bp
Low resolution profile in CG context for WT and met1-3
Alternatively, for a finer control, the user can use
computeMethylationProfile function to compute the
methylation profile at certain locations on the genome. This function
returns a Ranges object with four metadata columns
sumReadsM - the number of methylated reads
sumReadsN - the total number of reads
Proportion - the proportion of methylated reads
context - the context
One or more of these GRanges objects can be put in a
GRangesList object which is then passed as a parameter to
the plotMethylationProfile function.
regions <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(1,1E6))
# compute low resolution profile in 10 Kb windows
profileCGWT <- computeMethylationProfile(methylationDataList[["WT"]],
regions,
windowSize = 10000,
context = "CG")
profileCGMet13 <- computeMethylationProfile(methylationDataList[["met1-3"]],
regions,
windowSize = 10000,
context = "CG")
profilesCG <- GRangesList("WT" = profileCGWT, "met1-3" = profileCGMet13)
#plot the low resolution profile
par(mar=c(4, 4, 3, 1)+0.1)
par(mfrow=c(1,1))
plotMethylationProfile(profilesCG,
autoscale = FALSE,
labels = NULL,
title="CG methylation on Chromosome 3",
col=c("#D55E00","#E69F00"),
pch = c(1,0),
lty = c(4,1))Coverage of the bisulfite sequencing data
The number of reads from the bisulfite sequencing can differ
significantly between different locations on the genome in the sense
that cytosines in the same context (including neighbouring cytosines)
can display large variability in the coverage. To plot the coverage of
the bisulfite sequencing datasets, one can use
plotMethylationDataCoverage function which takes as input
one or two datasets and the vector with the thresholds used to compute
the proportion of cytosines with at least that many reads; see Figure
@ref(fig:coverage).
# plot the coverage in the two contexts
par(mar=c(4, 4, 3, 1)+0.1)
plotMethylationDataCoverage(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
breaks = c(1,5,10,15),
regions = NULL,
conditionsNames=c("WT","met1-3"),
context = c("CHH"),
proportion = TRUE,
labels=LETTERS,
contextPerRow = FALSE)
Coverage. For example, this figure shows that in WT only \(30%\) of the cytosines in CHH context have at least 10 reads
Alternatively, the DMRcaller also provides the
computeMethylationDataCoverage function which returns a
numeric vector with the number or proportion of cytosines in a specific
context that have at least a certain number of reads specified by the
input vector breaks.
Spatial correlation of methylation levels
Methylation levels are often spatially correlated and some methods to
detect DMRs assume this spatial correlation. Nevertheless, different
tissues, samples and even methylation context will display different
levels of correlation. DMRcaller implements
plotMethylationDataSpatialCorrelation function that plots
the correlation of methylation levels as function of distance between
cytosines. This function takes as input one or two datasets and the
vector with the distances between cytosines; see Figure
@ref(fig:correlationPlot).
# compute the spatial correlation of methylation levels
plotMethylationDataSpatialCorrelation(methylationDataList[["WT"]],
distances = c(1,100,10000), regions = NULL,
conditionsNames = c("WT"),
context = c("CG"),
labels = LETTERS, col = NULL,
pch = c(1,0,16,2,15,17), lty = c(4,1,3,2,6,5),
contextPerRow = FALSE,
log = "x")## Computing methylation levels correlation for distances of 1 bp
## Computing methylation levels correlation for distances of 100 bp
## Computing methylation levels correlation for distances of 10000 bp
Spatial correlation of methylation levels
Alternatively, the DMRcaller also provides the
computeMethylationDataSpatialCorrelation function which
returns a numeric vector with the correlation of methylation levels of
cytosines separated by certain distances in a specific context.
Calling DMRs
DMRcaller package provides computeDMRs function
to call DMRs. The output of this function is a GRanges with
11 metadata columns.
direction - a numeric value indicating whether the methylation was lost in the second condition compared to the first one (\(-1\)) or gained (\(+1\))
context - the context of the cytosine (CG, CHG or CHH)
sumReadsM1 - the number of methylated reads in the DMR in condition 1
sumReadsN1 - the total number of reads in the DMR in condition 1
proportion1 - the proportion of methylated reads in the DMR in condition 1
sumReadsM2 - the number of methylated reads in the DMR in condition 2
sumReadsN2 - the total number of reads in the DMR in condition 2
proportion2 - the proportion of methylated reads in the DMR in condition 2
cytosinesCount - the number of cytosines in the DMR
pValue - the adjusted p-value of the statistical test
regionType - a character string indicating whether the methylation was lost in the second condition compared to the first one (“loss”) or gained (“gain”)
For predifined regions (e.g. genes, transposons or CpG islands) the
user can call filterDMRs function to extract the list of
regions that are differentially methylated. The output of this function
is again a GRanges with the same 11 metadata columns.
Below we present examples of calling both functions.
chr_local <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(5E5,6E5))
# compute the DMRs in CG context with noise_filter method
DMRsNoiseFilterCG <- computeDMRs(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
regions = chr_local,
context = "CG",
method = "noise_filter",
windowSize = 100,
kernelFunction = "triangular",
test = "score",
pValueThreshold = 0.01,
minCytosinesCount = 4,
minProportionDifference = 0.4,
minGap = 0,
minSize = 50,
minReadsPerCytosine = 4,
cores = 1)## Parameters checking ...## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## Extract methylation in the corresponding context
## Computing DMRs at Chr3:500000..600000
## Calculating interpolations...
## Identifying DMRs...
## Analysed reads inside DMRs
## Merge DMRs iteratively
## Filter DMRs## GRanges object with 60 ranges and 11 metadata columns:
## seqnames ranges strand | direction context sumReadsM1
## <Rle> <IRanges> <Rle> | <numeric> <character> <numeric>
## [1] Chr3 503043-503148 * | -1 CG 299
## [2] Chr3 503390-503542 * | -1 CG 158
## [3] Chr3 503612-503901 * | -1 CG 342
## [4] Chr3 504042-504093 * | -1 CG 59
## [5] Chr3 504255-504348 * | -1 CG 265
## ... ... ... ... . ... ... ...
## [56] Chr3 593906-594076 * | -1 CG 216
## [57] Chr3 594128-594214 * | -1 CG 27
## [58] Chr3 594285-594385 * | -1 CG 128
## [59] Chr3 599027-599107 * | -1 CG 57
## [60] Chr3 599509-599634 * | -1 CG 168
## sumReadsN1 proportion1 sumReadsM2 sumReadsN2 proportion2 cytosinesCount
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 365 0.819178 0 419 0.00000000 10
## [2] 198 0.797980 0 414 0.00000000 12
## [3] 442 0.773756 3 807 0.00371747 25
## [4] 86 0.686047 0 249 0.00000000 6
## [5] 351 0.754986 0 412 0.00000000 12
## ... ... ... ... ... ... ...
## [56] 253 0.853755 1 648 0.00154321 16
## [57] 45 0.600000 2 107 0.01869159 4
## [58] 149 0.859060 0 258 0.00000000 4
## [59] 111 0.513514 3 154 0.01948052 4
## [60] 219 0.767123 0 201 0.00000000 8
## pValue regionType
## <numeric> <character>
## [1] 4.18251e-122 loss
## [2] 1.86673e-98 loss
## [3] 4.84005e-185 loss
## [4] 7.28326e-47 loss
## [5] 3.75353e-105 loss
## ... ... ...
## [56] 2.23081e-158 loss
## [57] 8.30991e-17 loss
## [58] 5.30109e-72 loss
## [59] 2.60868e-21 loss
## [60] 1.19821e-57 loss
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths# compute the DMRs in CG context with neighbourhood method
DMRsNeighbourhoodCG <- computeDMRs(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
regions = chr_local,
context = "CG",
method = "neighbourhood",
test = "score",
pValueThreshold = 0.01,
minCytosinesCount = 4,
minProportionDifference = 0.4,
minGap = 200,
minSize = 1,
minReadsPerCytosine = 4,
cores = 1)## Parameters checking ...## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## Extract methylation in the corresponding context
## Computing DMRs
## Merge DMRs iteratively
## Filter DMRs## GRanges object with 34 ranges and 16 metadata columns:
## seqnames ranges strand | context trinucleotide_context readsM1
## <Rle> <IRanges> <Rle> | <factor> <factor> <integer>
## [1] Chr3 503058-503853 * | CG CGG 96
## [2] Chr3 504058-504069 * | CG CGG 22
## [3] Chr3 504292-504490 * | CG CGA 35
## [4] Chr3 506440-506776 * | CG CGT 28
## [5] Chr3 507119-507480 * | CG CGA 6
## ... ... ... ... . ... ... ...
## [30] Chr3 588591-588633 * | CG CGC 11
## [31] Chr3 591681-591790 * | CG CGT 25
## [32] Chr3 593736-594337 * | CG CGA 65
## [33] Chr3 598934-599219 * | CG CGT 6
## [34] Chr3 599556-599586 * | CG CGA 46
## readsN1 readsM2 readsN2 pValue sumReadsM1 sumReadsN1
## <integer> <integer> <integer> <numeric> <numeric> <numeric>
## [1] 139 0 117 0.00000e+00 806 1217
## [2] 25 0 48 1.55222e-42 56 78
## [3] 39 0 42 6.07831e-180 389 584
## [4] 42 0 59 2.59969e-108 228 370
## [5] 9 0 21 1.10736e-102 225 415
## ... ... ... ... ... ... ...
## [30] 12 0 38 1.39683e-145 353 426
## [31] 31 2 59 1.27477e-143 268 296
## [32] 75 1 56 7.82820e-312 659 1068
## [33] 6 0 15 2.41968e-39 83 178
## [34] 55 0 63 4.16663e-57 166 217
## proportion1 sumReadsM2 sumReadsN2 proportion2 cytosinesCount direction
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 0.662284 4 2205 0.00181406 65 -1
## [2] 0.717949 0 210 0.00000000 5 -1
## [3] 0.666096 0 911 0.00000000 24 -1
## [4] 0.616216 3 629 0.00476948 20 -1
## [5] 0.542169 1 687 0.00145560 22 -1
## ... ... ... ... ... ... ...
## [30] 0.828638 4 509 0.00785855 8 -1
## [31] 0.905405 4 486 0.00823045 14 -1
## [32] 0.617041 6 1817 0.00330215 41 -1
## [33] 0.466292 3 331 0.00906344 13 -1
## [34] 0.764977 0 200 0.00000000 7 -1
## regionType
## <character>
## [1] loss
## [2] loss
## [3] loss
## [4] loss
## [5] loss
## ... ...
## [30] loss
## [31] loss
## [32] loss
## [33] loss
## [34] loss
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths# compute the DMRs in CG context with bins method
DMRsBinsCG <- computeDMRs(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
regions = chr_local,
context = "CG",
method = "bins",
binSize = 100,
test = "score",
pValueThreshold = 0.01,
minCytosinesCount = 4,
minProportionDifference = 0.4,
minGap = 200,
minSize = 50,
minReadsPerCytosine = 4,
cores = 1)## Parameters checking ...## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## Extract methylation in the corresponding context
## Computing DMRs at Chr3:500000..600000
## Count inside each bin...
## Filter the bins...
## Identifying DMRs...
## Merge adjacent DMRs
## Merge DMRs iteratively
## Filter DMRs## GRanges object with 40 ranges and 11 metadata columns:
## seqnames ranges strand | sumReadsM1 sumReadsN1 proportion1
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric>
## [1] Chr3 503000-503199 * | 299 731 0.409029
## [2] Chr3 503100-503199 * | 13 28 0.464286
## [3] Chr3 503400-503499 * | 158 198 0.797980
## [4] Chr3 503400-504499 * | 959 1674 0.572879
## [5] Chr3 506400-506699 * | 182 321 0.566978
## ... ... ... ... . ... ... ...
## [36] Chr3 593700-594399 * | 660 1151 0.573414
## [37] Chr3 599000-599299 * | 77 184 0.418478
## [38] Chr3 599200-599299 * | 20 35 0.571429
## [39] Chr3 599500-599599 * | 168 219 0.767123
## [40] Chr3 599500-599599 * | 168 219 0.767123
## sumReadsM2 sumReadsN2 proportion2 cytosinesCount context direction
## <numeric> <numeric> <numeric> <numeric> <character> <numeric>
## [1] 1 776 0.001288660 17 CG -1
## [2] 0 100 0.000000000 4 CG -1
## [3] 0 414 0.000000000 12 CG -1
## [4] 3 3183 0.000942507 90 CG -1
## [5] 3 546 0.005494505 18 CG -1
## ... ... ... ... ... ... ...
## [36] 7 1907 0.00367069 44 CG -1
## [37] 3 356 0.00842697 14 CG -1
## [38] 0 107 0.00000000 6 CG -1
## [39] 0 201 0.00000000 8 CG -1
## [40] 0 201 0.00000000 8 CG -1
## pValue regionType
## <numeric> <character>
## [1] 4.74723e-87 loss
## [2] 6.54241e-13 loss
## [3] 1.65932e-98 loss
## [4] 0.00000e+00 loss
## [5] 2.63625e-84 loss
## ... ... ...
## [36] 2.64404e-298 loss
## [37] 5.89785e-37 loss
## [38] 3.36772e-17 loss
## [39] 1.19821e-57 loss
## [40] 1.19821e-57 loss
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths# load the gene annotation data
data(GEs)
#select the genes
genes <- GEs[which(GEs$type == "gene")]
# compute the DMRs in CG context over genes
DMRsGenesCG <- filterDMRs(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
potentialDMRs = genes[overlapsAny(genes, chr_local)],
context = "CG",
test = "score",
pValueThreshold = 0.01,
minCytosinesCount = 4,
minProportionDifference = 0.4,
minReadsPerCytosine = 3,
cores = 1)## Parameters checking ...## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## Extract methylation in the corresponding context
## Computing DMRs at Chr3:101..999999
## Selecting data...
## Identifying DMRs...## GRanges object with 3 ranges and 21 metadata columns:
## seqnames ranges strand | source type score phase
## <Rle> <IRanges> <Rle> | <factor> <factor> <numeric> <integer>
## [1] Chr3 576378-579559 + | TAIR10 gene NA <NA>
## [2] Chr3 528574-532582 - | TAIR10 gene NA <NA>
## [3] Chr3 570134-572345 - | TAIR10 gene NA <NA>
## ID Name Note Parent Index
## <character> <character> <CharacterList> <CharacterList> <character>
## [1] AT3G02680 AT3G02680 protein_coding_gene <NA>
## [2] AT3G02530 AT3G02530 protein_coding_gene <NA>
## [3] AT3G02660 AT3G02660 protein_coding_gene <NA>
## Derives_from Alias sumReadsM1 sumReadsN1 proportion1 sumReadsM2
## <character> <CharacterList> <numeric> <numeric> <numeric> <numeric>
## [1] <NA> 1106 2379 0.464901 14
## [2] <NA> 1150 2756 0.417271 30
## [3] <NA> 874 1848 0.472944 10
## sumReadsN2 proportion2 cytosinesCount pValue regionType direction
## <numeric> <numeric> <numeric> <numeric> <character> <numeric>
## [1] 4546 0.00307963 142 0 loss -1
## [2] 6207 0.00483325 171 0 loss -1
## [3] 3487 0.00286779 104 0 loss -1
## -------
## seqinfo: 7 sequences from an unspecified genome; no seqlengthsMerge DMRs
Finally, for merging adjacent DMRs, DMRcaller provides the
function mergeDMRsIteratively which can be used as
follows:
DMRsNoiseFilterCGMerged <- mergeDMRsIteratively(DMRsNoiseFilterCG,
minGap = 200,
respectSigns = TRUE,
methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
context = "CG",
minProportionDifference = 0.4,
minReadsPerCytosine = 4,
pValueThreshold = 0.01,
test="score")## Parameters checking ...
## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## Merge DMRs iteratively ...## GRanges object with 37 ranges and 11 metadata columns:
## seqnames ranges strand | direction context sumReadsM1
## <Rle> <IRanges> <Rle> | <numeric> <character> <numeric>
## [1] Chr3 503043-503148 * | -1 CG 299
## [2] Chr3 503390-504509 * | -1 CG 959
## [3] Chr3 506392-506723 * | -1 CG 182
## [4] Chr3 507286-507422 * | -1 CG 153
## [5] Chr3 514791-514891 * | -1 CG 560
## ... ... ... ... . ... ... ...
## [33] Chr3 588556-588681 * | -1 CG 355
## [34] Chr3 591657-591828 * | -1 CG 268
## [35] Chr3 593709-594385 * | -1 CG 659
## [36] Chr3 599027-599107 * | -1 CG 57
## [37] Chr3 599509-599634 * | -1 CG 168
## sumReadsN1 proportion1 sumReadsM2 sumReadsN2 proportion2 cytosinesCount
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 365 0.819178 0 419 0.000000000 10
## [2] 1674 0.572879 3 3183 0.000942507 90
## [3] 321 0.566978 3 546 0.005494505 18
## [4] 217 0.705069 0 322 0.000000000 8
## [5] 760 0.736842 1 680 0.001470588 10
## ... ... ... ... ... ... ...
## [33] 458 0.775109 5 605 0.00826446 10
## [34] 321 0.834891 4 540 0.00740741 16
## [35] 1068 0.617041 6 1827 0.00328407 42
## [36] 111 0.513514 3 154 0.01948052 4
## [37] 219 0.767123 0 201 0.00000000 8
## pValue regionType
## <numeric> <character>
## [1] 4.18251e-122 loss
## [2] 0.00000e+00 loss
## [3] 1.44994e-84 loss
## [4] 1.20961e-70 loss
## [5] 2.03906e-178 loss
## ... ... ...
## [33] 4.02207e-150 loss
## [34] 4.83924e-140 loss
## [35] 5.44838e-314 loss
## [36] 2.60868e-21 loss
## [37] 1.19821e-57 loss
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsNote that two neighbouring DMRs will be merged if all the conditions below are met
they are within a distance from each other smaller than minGap
the difference in methylation levels between the two conditions is statistically significant according to the statistical test when the two DMRs are joined
the difference in methylation proportion between the two conditions is higher than a threshold value when the two DMRs are joined
the number of reads per cytosine is higher than a threshold when the two DMRs are joined
Calling DMRs using biological replicates
The package also contains a set of functions for the analysis of multiple biological replicates.
The synthetic dataset is made by 300 different cytosines, extracted
from those present in the A. thaliana dataset. The value for
readsN are created using the function
rnorm, while the values for readsM are
generated using the function rbinom. The probabilities used
are 0.1 in the external region and 0.8 in the central region. In this
way a DMR should be detected in the central region of the synthetic
dataset.
The difference in proportion is plotted in figure @ref(fig:differenceMethylationReplicates).
# loading synthetic data
data("syntheticDataReplicates")
# create vector with colours for plotting
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442",
"#0072B2", "#D55E00", "#CC79A7")
# plotting the difference in proportions
plot(start(syntheticDataReplicates),
syntheticDataReplicates$readsM1/syntheticDataReplicates$readsN1,
ylim=c(0,1), col=cbbPalette[2], xlab="Position in Chr3 (bp)",
ylab="Methylation proportion")
points(start(syntheticDataReplicates),
syntheticDataReplicates$readsM2/syntheticDataReplicates$readsN2,
col=cbbPalette[7], pch=4)
points(start(syntheticDataReplicates),
syntheticDataReplicates$readsM3/syntheticDataReplicates$readsN3,
col=cbbPalette[3], pch=2)
points(start(syntheticDataReplicates),
syntheticDataReplicates$readsM4/syntheticDataReplicates$readsN4,
col=cbbPalette[6], pch=3)
legend(x = "topleft", legend=c("Treatment 1", "Treatment 2", "Control 1",
"Control 2"), pch=c(1,4,2,3),
col=cbbPalette[c(2,7,3,6)], bty="n", cex=1.0)
Methylation proportions in the synthetic dataset.
The DMRs are computed using the function
computeDMRsReplicates, which uses beta regression (Ferrari &
Cribari-Neto, 2004) to detect differential methylation.
if (requireNamespace("GenomeInfoDb", quietly = TRUE)) {
# starting with data joined using joinReplicates
# creating condition vector
condition <- c("a", "a", "b", "b")
# computing DMRs using the neighbourhood method
DMRsReplicatesBins <- computeDMRsReplicates(methylationData = syntheticDataReplicates,
condition = condition,
regions = NULL,
context = "CG",
method = "bins",
binSize = 100,
test = "betareg",
pseudocountM = 1,
pseudocountN = 2,
pValueThreshold = 0.01,
minCytosinesCount = 4,
minProportionDifference = 0.4,
minGap = 0,
minSize = 50,
minReadsPerCytosine = 4,
cores = 1)
print(DMRsReplicatesBins)
}## Parameters checking ...## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## Extract methylation in the corresponding context
## Computing DMRs at Chr3:101..886
## Count inside each bin...
## Filter the bins...
## Identifying DMRs...
## Merge adjacent DMRs
## Merge DMRs iteratively
## Filter DMRs
## GRanges object with 2 ranges and 11 metadata columns:
## seqnames ranges strand | sumReadsM1 sumReadsN1 proportion1 sumReadsM2
## <Rle> <IRanges> <Rle> | <numeric> <numeric> <numeric> <numeric>
## [1] Chr3 401-500 * | 436 546 0.797445 61
## [2] Chr3 501-600 * | 419 521 0.803059 42
## sumReadsN2 proportion2 cytosinesCount context direction pValue
## <numeric> <numeric> <numeric> <character> <numeric> <numeric>
## [1] 596 0.103679 6 CG -1 0
## [2] 411 0.104116 4 CG -1 0
## regionType
## <character>
## [1] loss
## [2] loss
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsExtract methylation data in regions
analyseReadsInsideRegionsForCondition function can
extract additional information in a set of genomic regions (including
DMRs) from any methylationData object. For example, to
establish a link between the CG and CHH methylation, one might want to
extract the number of methylated reads and the total number of reads in
CHH context inside DMRs called in CG context.
#retrive the number of reads in CHH context in WT in CG DMRs
DMRsNoiseFilterCGreadsCHH <- analyseReadsInsideRegionsForCondition(
DMRsNoiseFilterCGMerged,
methylationDataList[["WT"]], context = "CHH",
label = "WT")## Parameters checking ...
## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## Extract methylation levels in corresponding context ...
## Compute reads inside each region ...## GRanges object with 37 ranges and 15 metadata columns:
## seqnames ranges strand | direction context sumReadsM1
## <Rle> <IRanges> <Rle> | <numeric> <character> <numeric>
## [1] Chr3 503043-503148 * | -1 CG 299
## [2] Chr3 503390-504509 * | -1 CG 959
## [3] Chr3 506392-506723 * | -1 CG 182
## [4] Chr3 507286-507422 * | -1 CG 153
## [5] Chr3 514791-514891 * | -1 CG 560
## ... ... ... ... . ... ... ...
## [33] Chr3 588556-588681 * | -1 CG 355
## [34] Chr3 591657-591828 * | -1 CG 268
## [35] Chr3 593709-594385 * | -1 CG 659
## [36] Chr3 599027-599107 * | -1 CG 57
## [37] Chr3 599509-599634 * | -1 CG 168
## sumReadsN1 proportion1 sumReadsM2 sumReadsN2 proportion2 cytosinesCount
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 365 0.819178 0 419 0.000000000 10
## [2] 1674 0.572879 3 3183 0.000942507 90
## [3] 321 0.566978 3 546 0.005494505 18
## [4] 217 0.705069 0 322 0.000000000 8
## [5] 760 0.736842 1 680 0.001470588 10
## ... ... ... ... ... ... ...
## [33] 458 0.775109 5 605 0.00826446 10
## [34] 321 0.834891 4 540 0.00740741 16
## [35] 1068 0.617041 6 1827 0.00328407 42
## [36] 111 0.513514 3 154 0.01948052 4
## [37] 219 0.767123 0 201 0.00000000 8
## pValue regionType sumReadsMWTCHH sumReadsNWTCHH proportionWTCHH
## <numeric> <character> <numeric> <numeric> <numeric>
## [1] 4.18251e-122 loss 0 303 0.00000000
## [2] 0.00000e+00 loss 99 3323 0.02979236
## [3] 1.44994e-84 loss 10 1047 0.00955110
## [4] 1.20961e-70 loss 0 571 0.00000000
## [5] 2.03906e-178 loss 1 665 0.00150376
## ... ... ... ... ... ...
## [33] 4.02207e-150 loss 12 672 0.01785714
## [34] 4.83924e-140 loss 6 792 0.00757576
## [35] 5.44838e-314 loss 29 2560 0.01132812
## [36] 2.60868e-21 loss 0 193 0.00000000
## [37] 1.19821e-57 loss 1 206 0.00485437
## cytosinesCountCHH
## <numeric>
## [1] 27
## [2] 309
## [3] 90
## [4] 33
## [5] 32
## ... ...
## [33] 32
## [34] 52
## [35] 196
## [36] 23
## [37] 36
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsCalling partially methylated domains (PMDs)
The DMRcaller package provides the computePMDs
function to identify partially methylated domains
(PMDs) from both WGBS and Oxford Nanopore methylation data.
PMDs are large genomic regions with intermediate methylation levels
(e.g., 40–60%) and can be identified using three available methods:
“noise_filter”, “neighbourhood”, and
“bins”
The output of this function is a GRanges object with five metadata columns:
context – the context of the cytosine (CG, CHG or CHH)
sumReadsM – the number of methylated reads in the PMD
sumReadsN – the total number of reads in the PMD
proportion – the average methylation proportion in the PMD (between minMethylation and maxMethylation)
cytosinesCount – the number of cytosines considered in the PMD
To compute PMDs in a specific region of interest, define a GRanges object. If not specified, PMDs are computed genome-wide.
# load the ONT methylation data
data(ontSampleGRangesList)
# define the region of interest on chr1
chr1_ranges <- GRanges(seqnames = Rle("chr1"), ranges = IRanges(1E6+5E5,2E6))
# compute the PMDs in CG context using the noise_filter method
PMDsNoiseFilterCG <- computePMDs(ontSampleGRangesList[["GM18501"]],
regions = chr1_ranges,
context = "CG",
method = "noise_filter",
windowSize = 100,
kernelFunction = "triangular",
lambda = 0.5,
minCytosinesCount = 4,
minMethylation = 0.4,
maxMethylation = 0.6,
minGap = 200,
minSize = 50,
minReadsPerCytosine = 4,
cores = 1,
parallel = FALSE)
PMDsNoiseFilterCG# compute the PMDs using the neighbourhood method
PMDsNeighbourhoodCG <- computePMDs(ontSampleGRangesList[["GM18501"]],
regions = chr1_ranges,
context = "CG",
method = "neighbourhood",
minCytosinesCount = 4,
minMethylation = 0.4,
maxMethylation = 0.6,
minGap = 200,
minSize = 50,
minReadsPerCytosine = 4,
cores = 1,
parallel = FALSE)
PMDsNeighbourhoodCG# compute the PMDs using the bins method
PMDsBinsCG <- computePMDs(ontSampleGRangesList[["GM18501"]],
regions = chr1_ranges,
context = "CG",
method = "bins",
binSize = 100,
minCytosinesCount = 4,
minMethylation = 0.4,
maxMethylation = 0.6,
minGap = 200,
minSize = 50,
minReadsPerCytosine = 4,
cores = 1,
parallel = FALSE)
PMDsBinsCGCalling variably methylated domains (VMDs) using ONT data
The DMRcaller package provides the computeVMDs
function to identify variably methylated domains (VMDs)
— genomic regions showing high or low variability in methylation levels
across reads at each cytosine. This is particularly relevant for
identifying regulatory instability or stochastic epigenetic variation
using Oxford Nanopore sequencing data.
The function segments the genome into fixed-size bins, evaluates the per-read methylation variability within each bin, and filters bins based on their weighted standard deviation (SD) using user-defined or data-driven thresholds.
The output is a GRanges object with the following metadata columns:
- context – the context in which the VMDs were
computed (“
CG”, “CHG”, or “CHH”) - sumReadsM – total number of methylated reads across the region
- sumReadsN – total number of reads across the region
- proportion – average methylation level across cytosines
- cytosinesCount – number of cytosines in the region
- mean – mean of per-read methylation proportions
- sd – standard deviation of per-read methylation proportions
- w_mean – weighted mean (based on read count per cytosine)
- w_sd – weighted standard deviation
To compute VMDs in a specific region or genome-wide, you must specify bin size and a method for selecting variance thresholds.
# load the ONT methylation data
data(ontSampleGRangesList)
# define the region of interest on chr1
chr1_ranges <- GRanges(seqnames = Rle("chr1"), ranges = IRanges(1E6+5E5, 1E6+6E5))
# compute the VMDs in CG context using the "EDE.high" variance threshold
VMDsBinsCG <- computeVMDs(ontSampleGRangesList[["GM18501"]],
regions = chr1_ranges,
context = "CG",
binSize = 100,
minCytosinesCount = 4,
sdCutoffMethod = "per.high", # elbow-detected high variance
percentage = 0.05, # only used if sdCutoffMethod = "per.high" or "per.low"
minGap = 200,
minSize = 50,
minReadsPerCytosine = 4,
parallel = FALSE,
cores = 1)
VMDsBinsCGThe sdCutoffMethod determines how to select high or low
variance regions:
- “
per.high” or “per.low” filter to top/bottom bins based on a quantile cutoff - “
EDE.high” or “EDE.low” apply an elbow detection algorithm by theinflection::edefrom package inflection to automatically determine the cutoff point in the SD distribution.
Detecting Variably Methylated Regions (VMRs) using ONT data
The function filterVMRsONT() is designed to identify
Variably Methylated Regions (VMRs) between two ONT
methylation datasets. These are genomic regions (e.g., genes,
transposable elements, or CpG islands) that exhibit statistically
significant differences in methylation variability or
methylation levels between two conditions.
This function applies the following ONT-specific statistical filters:
- Wilcoxon rank-sum test on per-read methylation proportions
- F-test on variance of per-read methylation proportions
- Optional: fold-change cutoffs on variance ratio, confidence interval filtering, and coverage/methylation thresholds
We use the example ONT methylation data and transcript annotation to identify VMRs across genes within a region of chromosome 1.
# load the ONT methylation data
data(ontSampleGRangesList)
# load the gene annotation data
data(GEs_hg38)
# select the transcript
transcript <- GEs_hg38[which(GEs_hg38$type == "transcript")]
# the regions where to compute the PMDs
regions <- GRanges(seqnames = Rle("chr1"), ranges = IRanges(1E6+5E5,2E6))
transcript <- transcript[overlapsAny(transcript, regions)]
# filter genes that are differntially methylated in the two conditions
VMRsGenesCG <- filterVMRsONT(ontSampleGRangesList[["GM18501"]],
ontSampleGRangesList[["GM18876"]], potentialVMRs = transcript,
context = "CG", pValueThreshold = 0.01,
minCytosinesCount = 4, minProportionDifference = 0.01,
minReadsPerCytosine = 3, ciExcludesOne = TRUE,
varRatioFc = NULL, parallel = TRUE) # parallel recommendedThe function returns a GRanges object containing the
same genomic intervals as the input potentialVMRs, enriched
with the following metadata columns:
- sumReadsM1 / sumReadsN1: Total methylated / total reads in condition 1
- sumReadsM2 / sumReadsN2: Total methylated / total reads in condition 2
- proportion1 / proportion2: Methylation proportions
- variance1 / variance2: Variance of per-read methylation levels
- wilcox_pvalue: FDR-adjusted p-value from Wilcoxon test
- f_pvalue: FDR-adjusted p-value from F-test
- var_ratio: Ratio of variances (condition1 / condition2)
- is_DMR: TRUE if Wilcoxon test passed significance threshold
- is_VMR: TRUE if F-test passed significance threshold
- regionType: “
gain” or “loss” of methylation - direction:
+1(gain) or-1(loss)
Only regions satisfying all filtering criteria (e.g., coverage,
cytosine count, variance CI exclusion, proportion difference) are
retained in the output. For strict variance detection, set
ciExcludesOne = TRUE and optionally apply
varRatioFc = 2 for 2-fold variance filtering.
Calling Co-Methylated Positions (CMPs) using ONT data
The DMRcaller package provides the
computeCoMethylatedPositions function to detect
pairwise co-methylation patterns between CpG, CpHpG or
CpHpH sites within user-defined genomic regions, such as
PMDs, genes, or repeat
elements. This is especially useful when exploring coordinated
methylation regulation or identifying methylation-linked chromatin
interactions using long-read Nanopore sequencing data.
For each region, the function computes strand-specific pairwise associations between methylation positions that fall within a specified genomic distance range. It constructs a 2×2 contingency table summarising methylation co-occurrence across reads and performs a statistical test to evaluate co-dependency.
The output is a list of GInteractions objects (one per
input region) containing significant pairs and their co-methylation
statistics.
Each significant interaction includes:
- C1_C2 – number of reads methylated at both cytosines
- C1_only – number of reads methylated only at the first cytosine
- C2_only – number of reads methylated only at the second cytosine
- neither – number of reads methylated at neither cytosines
- strand – DNA strand of the pair (“
+” or “-”) - genomic_position – region ID in UCSC/IGV format
(e.g. “
chr1:1522971-1523970”) - p.value – FDR-adjusted p-value from Fisher’s exact or permutation test
# load ONT methylation and PMD data
data("ont_gr_GM18870_chr1_PMD_bins_1k")
data("ont_gr_GM18870_chr1_sorted_bins_1k")
# compute co-methylation using Fisher's exact test
coMethylationFisher <- computeCoMethylatedPositions(
ont_gr_GM18870_chr1_sorted_bins_1k,
regions = ont_gr_GM18870_chr1_PMD_bins_1k[1:4],
minDistance = 150,
maxDistance = 1000,
minCoverage = 1,
pValueThreshold = 0.01,
test = "fisher",
parallel = FALSE)## Parameters checking ...
## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## [computeCoMethylatedPositions] PMD 1/4 (25.0%) at 18:28:59 | Elapsed: 0.0 sec
## [computeCoMethylatedPositions] PMD 2/4 (50.0%) at 18:29:07 | Elapsed: 8.3 sec
## [computeCoMethylatedPositions] PMD 3/4 (75.0%) at 18:29:17 | Elapsed: 18.2 sec
## [computeCoMethylatedPositions] PMD 4/4 (100.0%) at 18:29:23 | Elapsed: 24.5 sec
## [computeCoMethylatedPositions] Done! Total elapsed time: 31.4 sec## GInteractions object with 461 interactions and 7 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 | C1_C2 C1_only
## <Rle> <IRanges> <Rle> <IRanges> | <numeric> <numeric>
## [1] chr1 184990 --- chr1 185262 | 11 1
## [2] chr1 184990 --- chr1 185268 | 11 1
## [3] chr1 184990 --- chr1 185296 | 11 0
## [4] chr1 184990 --- chr1 185475 | 12 0
## [5] chr1 184990 --- chr1 185496 | 12 0
## ... ... ... ... ... ... . ... ...
## [457] chr1 789997 --- chr1 790607 | 14 1
## [458] chr1 790207 --- chr1 790412 | 11 3
## [459] chr1 790207 --- chr1 790632 | 11 2
## [460] chr1 790412 --- chr1 790607 | 11 0
## [461] chr1 790412 --- chr1 790632 | 10 1
## C2_only neither strand genomic_position p.value
## <numeric> <numeric> <character> <character> <numeric>
## [1] 0 6 + chr1:184969-186068 0.00513575
## [2] 0 6 + chr1:184969-186068 0.00513575
## [3] 0 6 + chr1:184969-186068 0.00223078
## [4] 0 6 + chr1:184969-186068 0.00176083
## [5] 0 5 + chr1:184969-186068 0.00338621
## ... ... ... ... ... ...
## [457] 1 6 - chr1:788869-790668 0.00733196
## [458] 0 9 - chr1:788869-790668 0.00500006
## [459] 0 8 - chr1:788869-790668 0.00399760
## [460] 4 11 - chr1:788869-790668 0.00391147
## [461] 2 11 - chr1:788869-790668 0.00733196
## -------
## regions: 304 ranges and 0 metadata columns
## seqinfo: 1 sequence from an unspecified genomeNote on region size and multiple testing correction
The computeCoMethylatedPositions function applies
FDR correction globally across all cytosine
pairs tested within the input regions. As such, the number and
range of regions provided can influence the adjusted p-values. For
example, providing smaller or fewer regions results in fewer total
comparisons, which can lead to more conservative (higher) adjusted
p-values for the same cytosine pairs.
Therefore, when designing region inputs for co-methylation analysis, it’s important to note that adjusted significance depends not only on the pairwise test but also on the size and number of regions analysed.
Calling Co-Methylated Regions (CMRs) using ONT Data
The DMRcaller package also provides the
computeCoMethylatedRegions function to detect
correlation between methylation levels of different
genomic regions, such as PMDs, genes,
or regulatory domains, using long-read Nanopore
methylation data. This approach identifies region pairs that are
coordinated in their methylation profiles across single
reads. For example, two PMDs showing a high positive
correlation may reflect shared regulatory influence,
spatial proximity, or joint epigenetic repression.
For each candidate region pair within the user-defined distance range (e.g. 500–50,000.bp), the function computes:
- Per-read methylation proportions across each region
- Correlation coefficients (Pearson, Spearman, or Kendall) between these proportions
- FDR-adjusted p-values to identify significantly co-methylated region pairs
The function returns a GInteractions object, where:
- Each row represents a significantly correlated region pair
- Anchors (
anchor1andanchor2) correspond to the original inputregions - Metadata columns include:
correlation– correlation coefficient (e.g. Pearson’s r)coverage– number of shared reads used for correlationp.value– FDR-adjusted p-valuegenomic_position– string representation of both region coordinates
# load ONT methylation and PMD data
data("ont_gr_GM18870_chr1_sorted_bins_1k")
data("ont_gr_GM18870_chr1_PMD_bins_1k")
# compute highly correlated PMD region pairs using Pearson method
coMethRegions <- computeCoMethylatedRegions(
methylationData = ont_gr_GM18870_chr1_sorted_bins_1k,
regions = ont_gr_GM18870_chr1_PMD_bins_1k[1:5],
minDistance = 500,
maxDistance = 50000,
minCoverage = 4,
pValueThreshold = 0.05,
correlation_test = "pearson",
minCorrelation = -0.5,
maxCorrelation = 0.5,
parallel = FALSE
)## [computeCoMethylatedRegions] Parameters checking ...
## Current parallel setting, BPPARAM:
## class: SerialParam
## bpisup: FALSE; bpnworkers: 1; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: FALSE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: FALSE
## bplogdir: NA
## bpresultdir: NA
## [computeCoMethylatedRegions] Count all case of pairs...
## [computeCoMethylatedRegions] Compute Correlated Methylated Regions...
## | | | 0% | |======================================================================| 100%
##
## [computeCoMethylatedRegions] Filter Correlated Methylated Regions
## [computeCoMethylatedRegions] Done! Total elapsed time: 0.2 sec## GInteractions object with 1 interaction and 15 metadata columns:
## seqnames1 ranges1 seqnames2 ranges2 | anchor1.sumReadsM
## <Rle> <IRanges> <Rle> <IRanges> | <numeric>
## [1] chr1 910569-911568 --- chr1 913669-914968 | 750
## anchor1.sumReadsN anchor1.proportion anchor1.cytosinesCount
## <numeric> <numeric> <numeric>
## [1] 1425 0.526316 70
## anchor1.context anchor1.direction anchor2.sumReadsM anchor2.sumReadsN
## <character> <numeric> <numeric> <numeric>
## [1] CG 1 776 1559
## anchor2.proportion anchor2.cytosinesCount anchor2.context
## <numeric> <numeric> <character>
## [1] 0.497755 72 CG
## anchor2.direction coverage correlation p.value
## <numeric> <integer> <numeric> <numeric>
## [1] 1 32 0.609905 0.000210602
## -------
## regions: 2 ranges and 0 metadata columns
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsPlotting the distribution of DMRs, PMDs, VMDs or VMRs
Sometimes, it is useful to obtain the distribution of the DMRs over
the chromosomes. The DMRcaller provides the
computeOverlapProfile function, which computes this
distribution. The GRanges object generated by this function
can then be added to a GRangesList object, which can be
plotted using plotOverlapProfile function; see Figure
@ref(fig:DMRdensity). Additionally, the plotOverlapProfile
function allows the user to specify two GRangesList, thus,
allowing the plotting of distributions of hypo or hyper methylated DMRs
separately.
# compute the distribution of DMRs
hotspots <- computeOverlapProfile(DMRsNoiseFilterCGMerged, chr_local,
windowSize=5000, binary=TRUE)## Calculating overlaps for Chr3:500000..600000 using a window of 5000 bp
Distribution of DMRs. Darker colour indicates higher density, while lighter colour lower density.
Plotting profiles with DMRs, PMDs, VMDs or VMRs
Finally, DMRcaller package also provides a function to plot
methylation profiles at a specific location on the genome. To plot the
methylation profile the user needs to call the
plotLocalMethylationProfile function; see Figure
@ref(fig:localMethylationProfile).
# select a 20 Kb location on the Chr3
chr3Reg <- GRanges(seqnames = Rle("Chr3"), ranges = IRanges(510000,530000))
# create a list with all DMRs
DMRsCGList <- list("noise filter" = DMRsNoiseFilterCGMerged,
"neighbourhood" = DMRsNeighbourhoodCG,
"bins" = DMRsBinsCG,
"genes" = DMRsGenesCG)
# plot the local profile
par(cex=0.9)
par(mar=c(4, 4, 3, 1)+0.1)
plotLocalMethylationProfile(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
chr3Reg,
DMRsCGList,
conditionsNames = c("WT", "met1-3"),
GEs,
windowSize = 300,
main="CG methylation")
Local methylation profile. The points on the graph represent methylation
proportion of individual cytosines, their colour (red or blue) which
sample they belong to and the intensity of the colour how many reads
that particular cytosine had. This means that darker colours indicate
stronger evidence that the corresponding cytosine has the corresponding
methylation proportion, while lighter colours indicate a weaker
evidence. The solid lines represent the smoothed profiles and the
intensity of the colour the coverage at the corresponding position
(darker colours indicate more reads while lighter ones less reads). The
boxes on top represent the DMRs, where a filled box will represent a DMR
which gained methylation while a box with a pattern represent a DMR that
lost methylation. The DMRs need to have a metadata column
regionType which can be either gain (where
there is more methylation in condition 2 compared to condition 1) or
loss (where there is less methylation in condition 2
compared to condition 1). In case this metadata column is missing all
DMRs are drawn using filled boxes. Finally we also allow annotation of
the DNA sequence. We represent by black boxes all the exons, which are
joined by a horizontal black line, thus, marking the full body of the
gene. With grey boxes we mark the transposable elements. Both for genes
and transposable elements we plot them over a mid line if they are on
the positive strand and under the mid line if they are on the negative
strand.
Parallel computation
Computing the DMRs can be computationally intensive. For example, in the case of A. thaliana (with a genome of \(\approx 130\ Mb\)), it can take several hours to compute the DMRs depending on the method used and on the number of DMRs. To speed up computations, DMRcaller supports parallel computing of DMRs using the package BiocParallel, which able to run parallel works in any types of processor (including Windows, Mac, Linux and HPC conditions).
# Parallel; compute the DMRs in CG context with noise_filter method
DMRsNoiseFilterCG_3cores <- computeDMRs(methylationDataList[["WT"]],
methylationDataList[["met1-3"]],
regions = chr_local,
context = "CG",
method = "noise_filter",
windowSize = 100,
kernelFunction = "triangular",
test = "score",
pValueThreshold = 0.01,
minCytosinesCount = 4,
minProportionDifference = 0.4,
minGap = 0,
minSize = 50,
minReadsPerCytosine = 4,
parallel = TRUE, # default: FALSE
BPPARAM = NULL, # default: NULL
cores = 3 # default: 1
) ## Parameters checking ...
## Local Unix -> using MulticoreParam() with forking and 4 workers## Current parallel setting, BPPARAM:
## class: MulticoreParam
## bpisup: FALSE; bpnworkers: 3; bptasks: 2147483647; bpjobname: BPJOB
## bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
## bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
## bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: FALSE
## bpfallback: TRUE
## bplogdir: NA
## bpresultdir: NA
## cluster type: FORK
## Extract methylation in the corresponding context
## Compute the DMRs using 3 cores
## | | | 0%## | |======================= | 33%## | |=============================================== | 67%## | |======================================================================| 100%
##
## Analysed reads inside DMRs
## | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100%
##
## Merge DMRs iteratively
## Filter DMRs## GRanges object with 60 ranges and 11 metadata columns:
## seqnames ranges strand | direction context sumReadsM1
## <Rle> <IRanges> <Rle> | <numeric> <character> <numeric>
## [1] Chr3 503043-503148 * | -1 CG 299
## [2] Chr3 503390-503542 * | -1 CG 158
## [3] Chr3 503612-503901 * | -1 CG 342
## [4] Chr3 504042-504093 * | -1 CG 59
## [5] Chr3 504255-504348 * | -1 CG 265
## ... ... ... ... . ... ... ...
## [56] Chr3 593906-594076 * | -1 CG 216
## [57] Chr3 594128-594214 * | -1 CG 27
## [58] Chr3 594285-594385 * | -1 CG 128
## [59] Chr3 599027-599107 * | -1 CG 57
## [60] Chr3 599509-599634 * | -1 CG 168
## sumReadsN1 proportion1 sumReadsM2 sumReadsN2 proportion2 cytosinesCount
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 365 0.819178 0 419 0.00000000 10
## [2] 198 0.797980 0 414 0.00000000 12
## [3] 442 0.773756 3 807 0.00371747 25
## [4] 86 0.686047 0 249 0.00000000 6
## [5] 351 0.754986 0 412 0.00000000 12
## ... ... ... ... ... ... ...
## [56] 253 0.853755 1 648 0.00154321 16
## [57] 45 0.600000 2 107 0.01869159 4
## [58] 149 0.859060 0 258 0.00000000 4
## [59] 111 0.513514 3 154 0.01948052 4
## [60] 219 0.767123 0 201 0.00000000 8
## pValue regionType
## <numeric> <character>
## [1] 4.18251e-122 loss
## [2] 1.86673e-98 loss
## [3] 4.84005e-185 loss
## [4] 7.28326e-47 loss
## [5] 3.75353e-105 loss
## ... ... ...
## [56] 2.23081e-158 loss
## [57] 8.30991e-17 loss
## [58] 5.30109e-72 loss
## [59] 2.60868e-21 loss
## [60] 1.19821e-57 loss
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsThe five functions used for computing and filtering the DMRs, PMDs
and VMRs (readONTbam, computeDMRs,
filterDMRs, mergeDMRsIteratively,
analyseReadsInsideRegionsForCondition,
computeDMRsReplicates,computePMDs,
filterPMDs, mergePMDsIteratively,
analyseReadsInsideRegionsForConditionPMD,
filterVMRsONT, computeCoMethylatedPositions
and computeCoMethylatedRegions) accept three parameters
parallel- run in parallel ifTRUE.; default setting:FALSEBPPARAM- ABiocParallelParamobject controlling parallel execution. This value will automatically set when parallel isTRUE, also able to set as manually.; default setting:NULLcores- which specifies the number of cores that can be used when performing the corresponding computations (must not exceedBPPARAM$workers). This value will automatically set as the maximum number of system workers, also able to set as manually.; default setting:1
When using 10 cores, it can take between 10 and 30 minutes to compute the DMRs in A. thaliana depending on the selected parameters.
Session information
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] DMRcaller_1.43.1 GenomicRanges_1.63.1 Seqinfo_1.1.0
## [4] IRanges_2.45.0 S4Vectors_0.49.0 BiocGenerics_0.57.0
## [7] generics_0.1.4 bookdown_0.46
##
## loaded via a namespace (and not attached):
## [1] sandwich_3.1-1 sass_0.4.10
## [3] SparseArray_1.11.10 bitops_1.0-9
## [5] stringi_1.8.7 lattice_0.22-7
## [7] magrittr_2.0.4 digest_0.6.39
## [9] evaluate_1.0.5 grid_4.5.2
## [11] fastmap_1.2.0 jsonlite_2.0.0
## [13] Matrix_1.7-4 inflection_1.3.7
## [15] cigarillo_1.1.0 GenomeInfoDb_1.47.2
## [17] nnet_7.3-20 Formula_1.2-5
## [19] httr_1.4.7 UCSC.utils_1.7.1
## [21] Biostrings_2.79.4 modeltools_0.2-24
## [23] codetools_0.2-20 jquerylib_0.1.4
## [25] abind_1.4-8 cli_3.6.5
## [27] rlang_1.1.7 crayon_1.5.3
## [29] XVector_0.51.0 Biobase_2.71.0
## [31] betareg_3.2-4 cachem_1.1.0
## [33] DelayedArray_0.37.0 yaml_2.3.12
## [35] S4Arrays_1.11.1 flexmix_2.3-20
## [37] tools_4.5.2 parallel_4.5.2
## [39] BiocParallel_1.45.0 Rsamtools_2.27.0
## [41] InteractionSet_1.39.0 SummarizedExperiment_1.41.0
## [43] buildtools_1.0.0 R6_2.6.1
## [45] zoo_1.8-15 matrixStats_1.5.0
## [47] lifecycle_1.0.5 stringr_1.6.0
## [49] bslib_0.9.0 RcppRoll_0.3.1
## [51] glue_1.8.0 Rcpp_1.1.1
## [53] lmtest_0.9-40 xfun_0.56
## [55] GenomicAlignments_1.47.0 sys_3.4.3
## [57] MatrixGenerics_1.23.0 knitr_1.51
## [59] htmltools_0.5.9 rmarkdown_2.30
## [61] maketools_1.3.2 compiler_4.5.2References
- Introduction
- Methods
- Description
- Data
- Low resolution profiles
- Coverage of the bisulfite sequencing data
- Spatial correlation of methylation levels
- Calling DMRs
- Merge DMRs
- Calling DMRs using biological replicates
- Extract methylation data in regions
- Calling partially methylated domains (PMDs)
- Calling variably methylated domains (VMDs) using ONT data
- Detecting Variably Methylated Regions (VMRs) using ONT data
- Calling Co-Methylated Positions (CMPs) using ONT data
- Calling Co-Methylated Regions (CMRs) using ONT Data
- Plotting the distribution of DMRs, PMDs, VMDs or VMRs
- Plotting profiles with DMRs, PMDs, VMDs or VMRs
- Parallel computation
- Session information
- References