--- 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 --- ## Version Compatibility Reference examples tested with: GenomicRanges 1.54+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('')` 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

bio-methylation-dmr-detection

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