# Bio Chipseq Differential Binding

Differential binding analysis using DiffBind.

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-07-21
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-differential-binding -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-differential-binding -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-differential-binding -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-differential-binding -g -a gemini-cli -y`

## Description

Differential binding analysis using DiffBind. Compare ChIP-seq peaks between conditions with statistical rigor. Requires replicate samples. Outputs differentially bound regions with fold changes and p-values. Use when comparing ChIP-seq binding between conditions.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Version Compatibility

Reference examples tested with: DESeq2 1.42+, edgeR 4.0+

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.

# Differential Binding with DiffBind

**"Compare ChIP-seq binding between conditions"** → Identify genomic regions with statistically significant differences in transcription factor or histone mark occupancy between experimental groups.
- R: `DiffBind::dba()` → `dba.count()` → `dba.contrast()` → `dba.analyze()`

## Create Sample Sheet

**Goal:** Define the experimental design linking BAM files, peak files, and sample metadata for DiffBind.

**Approach:** Build a data frame (or CSV) with required columns mapping each sample to its files and conditions.

```r
# Create sample sheet as data frame or CSV
samples <- data.frame(
    SampleID = c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2'),
    Tissue = c('cell', 'cell', 'cell', 'cell'),
    Factor = c('H3K4me3', 'H3K4me3', 'H3K4me3', 'H3K4me3'),
    Condition = c('control', 'control', 'treatment', 'treatment'),
    Replicate = c(1, 2, 1, 2),
    bamReads = c('ctrl1.bam', 'ctrl2.bam', 'treat1.bam', 'treat2.bam'),
    Peaks = c('ctrl1_peaks.narrowPeak', 'ctrl2_peaks.narrowPeak',
              'treat1_peaks.narrowPeak', 'treat2_peaks.narrowPeak'),
    PeakCaller = c('macs', 'macs', 'macs', 'macs')
)

write.csv(samples, 'samples.csv', row.names = FALSE)
```

## Load Data

**Goal:** Initialize a DiffBind object from the sample sheet containing all samples and peaks.

**Approach:** Read the sample sheet CSV into a DBA object that identifies overlapping peaks across samples.

```r
library(DiffBind)

# From sample sheet
dba_obj <- dba(sampleSheet = 'samples.csv')

# View summary
dba_obj
```

## Count Reads in Peaks

The rest: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/blob/HEAD/skills/bio-chipseq-differential-binding/SKILL.md