bio-chipseq-differential-binding

lilinji/GeneTind-Life-Skills

SkillLoads cleanly

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR…

Scores
out of 100 · grade B+

2026-08-22

Works

40% of the score

100/100

Loads cleanly: valid frontmatter, required fields present, no dangling references.

Maintained

25% of the score

94/100

no commits in the last 12 weeks
no license file

Adopted

20% of the score

13/100

8 stars on the source repo.

Documented

15% of the score

91/100

2,746 words with worked examples.
Ships 5 bundled files.

Loads cleanly. Every structural check Claude Code needs to register this passed. Last commit today.

Install

npx skills add lilinji/GeneTind-Life-Skills/bio-chipseq-differential-binding

What the check found

Every finding on this listing, and what each one means for whether it loads.
Finding	What it means
No license	The repository ships no license file, so the reuse terms are unclear.

What it says it does

Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

Every number above came out of reading the file and its repository. Nothing is a judgement call, nothing is a model’s opinion, and nothing can be paid for. The full method is published. Source last committed 2026-08-21.

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