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bio-methylation-dmr-detection

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-methylation-dmr-detection🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatindifferentially

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

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

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

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

Source Doc

Excerpt From SKILL.md

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')