Agent skill

bio-methylation-dmr-detection

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.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-methylation-dmr-detection

SKILL.md

Version Compatibility

Reference examples tested with: GenomicRanges 1.54+

Before using code patterns, verify installed versions match. If versions differ:

  • R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

DMR Detection

"Find differentially methylated regions" → Identify contiguous genomic regions with statistically significant methylation differences between conditions using tiling, smoothing, or kernel-based approaches.

  • R: methylKit::tileMethylCounts() + calculateDiffMeth(), bsseq::BSmooth(), DMRcate::dmrcate()

methylKit Tile-Based DMRs

r
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

r
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

r
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

Goal: Map differentially methylated regions to overlapping genes, promoters, and CpG islands for biological interpretation.

Approach: Build a genome annotation set with annotatr, convert DMRs to GRanges, and intersect with genomic features to classify each DMR by functional context.

r
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

r
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

r
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

r
library(GenomicRanges)

dmr_gr <- as(dmrs, 'GRanges')

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

Export DMRs

r
# 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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