# Bio Atac Seq Differential Accessibility

Find differentially accessible chromatin regions between conditions using DiffBind or...

- Repository: https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills (3090 stars)
- Category: Design
- 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-atac-seq-differential-accessibility -g -a claude-code -y`
Codex: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-differential-accessibility -g -a codex -y`
Cursor: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-differential-accessibility -g -a cursor -y`
Gemini CLI: `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-differential-accessibility -g -a gemini-cli -y`

## Description

Find differentially accessible chromatin regions between conditions using DiffBind or DESeq2. Use when comparing chromatin accessibility between treatment groups, cell types, or developmental stages in ATAC-seq experiments.

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

## Version Compatibility

Reference examples tested with: DESeq2 1.42+, GenomicRanges 1.54+, Subread 2.0+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scipy 1.12+

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.

# Differential Accessibility

**"Find differentially accessible regions between my conditions"** → Identify chromatin regions with statistically significant changes in accessibility between treatment groups, cell types, or timepoints.
- R: `DiffBind` or `DESeq2` on a peak-by-sample count matrix

## DiffBind Workflow

**Goal:** Identify differentially accessible chromatin regions between experimental conditions.

**Approach:** Load sample metadata and peak files into DiffBind, count reads in consensus peaks, normalize, define contrasts, and run differential analysis with DESeq2 backend.

```r
library(DiffBind)

# 1. Create sample sheet
samples <- data.frame(
    SampleID = c('ctrl_1', 'ctrl_2', 'treat_1', 'treat_2'),
    Condition = c('control', 'control', 'treated', 'treated'),
    Replicate = c(1, 2, 1, 2),
    bamReads = c('ctrl_1.bam', 'ctrl_2.bam', 'treat_1.bam', 'treat_2.bam'),
    Peaks = c('ctrl_1.narrowPeak', 'ctrl_2.narrowPeak', 'treat_1.narrowPeak', 'treat_2.narrowPeak')
)
write.csv(samples, 'samples.csv', row.names=FALSE)

# 2. Load data
dba <- dba(sampleSheet='samples.csv')

# 3. Count reads
dba <- dba.count(dba)

# 4. Normalize
dba <- dba.normalize(dba)

# 5. Set up contrasts
dba <- dba.contrast(dba, contrast=c('Condition', 'treated', 'control'))

# 6. Differential analysis
dba <- dba.analyze(dba)

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