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bio-atac-seq-differential-accessibility

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-atac-seq-differential-accessibility🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatinfind

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-atac-seq-differential-accessibility

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: DiffBind or DESeq2 on a peak-by-sample count matrix.
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.
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).

Source Doc

Excerpt From SKILL.md

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.

library(DiffBind)

## 2. Load data

dba <- dba(sampleSheet='samples.csv')

## 5. Set up contrasts

dba <- dba.contrast(dba, contrast=c('Condition', 'treated', 'control'))