数据与复现生物信息与基因组学FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

bio-methylation-dmr-detection

维护者 FreedomIntelligence · 最近更新 2026年4月1日

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.

OpenClawNanoClaw分析处理复现实验bio-methylation-dmr-detection🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatindifferentially

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者: FreedomIntelligence
许可: MIT
最近更新: 2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

R：methylKit：：tileMethylCounts() + calculateDiffMeth()，bsseq：：BSmooth()，DMRcate：：dmrcate()。
Find differentially methylated regions" → Identify contiguous genomic regions ，支持 statistically significant methylation differences between conditions ，使用 tiling，smoothing，或 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)。

原始文档

SKILL.md 摘录

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')