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.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-code
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-methylation-dmr-detection --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 6 KB
Bundled scripts: none
Path: skills/bio-methylation-dmr-detection/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the 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

What's inside
Steps it walks through
  1. Version Compatibility
  2. methylKit Tile-Based DMRs
  3. bsseq BSmooth DMRs
  4. DMRcate Method
  5. Annotate DMRs with Genes
  6. Annotate with genomation
  7. Visualize DMR
  8. Merge Adjacent DMRs
  9. Export DMRs
  10. DMR Comparison Across Methods
  11. Key Parameters
  12. methylKit tileMethylCounts
  13. bsseq dmrFinder
  14. Related Skills
Ships with 2 files
  • examples/dmr_methylkit.R
  • usage-guide.md
More from OpenClaw-Medical-Skills
All skills →
About this skill
What does the bio-methylation-dmr-detection skill do?

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.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-methylation-dmr-detection --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

Keep going