bio-chipseq-differential-binding
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-binding --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: DiffBind 3.20+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, PyDESeq2 0.4+, NormR 1.28+, MAnorm2 1.2+, ChIPseqSpikeInFree 1.6+. DiffBind 3.0+ changed defaults: `summits=200` (was FALSE), `dba.normalize()` now required, blacklist filtering on by default, full library size normalization replaces reads-in-peaks. Always run `packageVersion('DiffBind')` and inspect `dba.normalize(obj, bRetrieve=TRUE)` to confirm what was applied. # Differential ChIP-seq Binding **"Compare protein-DNA binding between experimental conditions"** -> Identify regions where IP signal changes significantly, accounting for sequencing depth, composition bias, trended biases, and global shifts that confound naive normalization. - R (BAM + peaks): `DiffBind::dba()` -> `dba.count()` -> `dba.normalize()` -> `dba.analyze()` - R (count matrix): `DESeq2::DESeq()` or `edgeR::glmQLFTest()` on a peaks-by-samples matrix - R (windows-based, global-shift-robust): `csaw::windowCounts()` -> `csaw::normFactors()` -> `edgeR::glmQLFTest()` - R (control-aware): `normr::diffR(chip1.bam, chip2.bam, genome)` joint binomial mixture - Python (count matrix): `pydeseq2.DeseqDataSet()` Cho
- Version Compatibility
- The Three Distinct Normalization Problems
- Algorithmic Taxonomy
- Decision Tree: Choosing Normalization
- Spike-In Scaling Factor Calculation
- DiffBind Workflow (BAMs + Peaks)
- csaw Workflow (Windows-Based)
- DESeq2 from Count Matrix
- Per-Tool Failure Modes
- DiffBind -- summits=200 default destroys broad marks
- DiffBind -- DBANORMRLE reads-in-peaks default reverses global shifts
- DESeq2 -- Pre-filtering removes condition-specific peaks
- csaw -- Trended bias loess over-normalizes biology
- Spike-in normalization -- Scaling factor applied to wrong layer
What does the bio-chipseq-differential-binding skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill differential-binding --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.