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name: bio-methylation-dmr-detection description: Differentially methylated region (DMR) detection using methylKit tiles, bsseq BSmooth, and DMRcate. Use when identifying contiguous genomic regions with methylation differences between experimental conditions or cell types. tool_type: r primary_tool: methylKit measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

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DMR Detection

methylKit Tile-Based DMRs

library(methylKit)

# Read and process data
meth_obj <- methRead(location = file_list, sample.id = sample_ids, treatment = treatment,
                      assembly = 'hg38', pipeline = 'bismarkCoverage')
meth_filt <- filterByCoverage(meth_obj, lo.count = 10, hi.perc = 99.9)

# Create tiles (windows)
tiles <- tileMethylCounts(meth_filt, win.size = 1000, step.size = 1000, cov.bases = 3)

tiles_united <- unite(tiles, destrand = TRUE)

# Differential methylation on tiles
diff_tiles <- calculateDiffMeth(tiles_united, overdispersion = 'MN', mc.cores = 4)

# Get significant DMRs
dmrs <- getMethylDiff(diff_tiles, difference = 25, qvalue = 0.01)
dmrs_hyper <- getMethylDiff(diff_tiles, difference = 25, qvalue = 0.01, type = 'hyper')
dmrs_hypo <- getMethylDiff(diff_tiles, difference = 25, qvalue = 0.01, type = 'hypo')

bsseq BSmooth DMRs

library(bsseq)

# Read Bismark cytosine reports
bs <- read.bismark(files = c('sample1.CpG_report.txt.gz', 'sample2.CpG_report.txt.gz'),
                    sampleNames = c('ctrl', 'treat'),
                    rmZeroCov = TRUE,
                    strandCollapse = TRUE)

# Smooth methylation data
bs_smooth <- BSmooth(bs, mc.cores = 4, verbose = TRUE)

# Filter by coverage
bs_cov <- getCoverage(bs_smooth)
keep <- which(rowSums(bs_cov >= 2) == ncol(bs_cov))
bs_filt <- bs_smooth[keep, ]

# Find DMRs with BSmooth
dmrs_bsseq <- dmrFinder(bs_filt, cutoff = c(-0.1, 0.1), stat = 'tstat.corrected')

DMRcate Method

library(DMRcate)
library(minfi)

# From methylation matrix (beta values)
# Rows = CpGs, columns = samples
design <- model.matrix(~ treatment)

# Run DMRcate
myannotation <- cpg.annotate('array', meth_matrix, what = 'Beta', arraytype = 'EPIC',
                               design = design, coef = 2)

dmr_results <- dmrcate(myannotation, lambda = 1000, C = 2)
dmr_ranges <- extractRanges(dmr_results)

Annotate DMRs with Genes

library(annotatr)

# Build annotations
annots <- build_annotations(genome = 'hg38', annotations = c(
    'hg38_basicgenes',
    'hg38_genes_promoters',
    'hg38_cpg_islands'
))

# Convert DMRs to GRanges
dmr_gr <- as(dmrs, 'GRanges')

# Annotate
dmr_annotated <- annotate_regions(regions = dmr_gr, annotations = annots, ignore.strand = TRUE)
dmr_df <- data.frame(dmr_annotated)

Annotate with genomation

library(genomation)

# Read gene annotations
gene_obj <- readTranscriptFeatures('genes.bed12')

# Annotate DMRs
dmr_gr <- as(dmrs, 'GRanges')
annot_result <- annotateWithGeneParts(dmr_gr, gene_obj)

# Get promoter/exon/intron breakdown
getTargetAnnotationStats(annot_result, percentage = TRUE, precedence = TRUE)

Visualize DMR

library(Gviz)

# Create track for a DMR
chr <- 'chr1'
start <- 1000000
end <- 1010000

# Methylation data track
meth_track <- DataTrack(
    range = bs_smooth,
    genome = 'hg38',
    name = 'Methylation',
    type = 'smooth'
)

# Gene annotation track
gene_track <- GeneRegionTrack(TxDb.Hsapiens.UCSC.hg38.knownGene, genome = 'hg38', name = 'Genes')

# Plot
plotTracks(list(meth_track, gene_track), from = start, to = end, chromosome = chr)

Merge Adjacent DMRs

library(GenomicRanges)

dmr_gr <- as(dmrs, 'GRanges')

# Merge DMRs within 500bp
dmr_merged <- reduce(dmr_gr, min.gapwidth = 500)

Export DMRs

# To BED
library(rtracklayer)
export(dmr_gr, 'dmrs.bed', format = 'BED')

# To CSV
dmr_df <- getData(dmrs)
write.csv(dmr_df, 'dmrs.csv', row.names = FALSE)

# To GFF
export(dmr_gr, 'dmrs.gff3', format = 'GFF3')

DMR Comparison Across Methods

Method	Package	Approach	Best For
Tiles	methylKit	Fixed windows	Quick analysis
BSmooth	bsseq	Smoothing	WGBS data
DMRcate	DMRcate	Kernel smoothing	Array data
DSS	DSS	Bayesian	Complex designs

Key Parameters

methylKit tileMethylCounts

Parameter	Default	Description
win.size	1000	Window size (bp)
step.size	1000	Step size (bp)
cov.bases	0	Min CpGs per tile

bsseq dmrFinder

Parameter	Description
cutoff	Methylation difference threshold
stat	Statistic to use
maxGap	Max gap between CpGs

Related Skills

• methylkit-analysis - Single CpG analysis
• methylation-calling - Generate input files
• pathway-analysis/go-enrichment - Functional annotation of DMR genes
• differential-expression/deseq2-basics - Compare with expression changes

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