Data & ReproStatistics & Data AnalysisFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction

bio-chipseq-differential-binding

Maintainer FreedomIntelligence · Last updated April 1, 2026

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.

OpenClawNanoClawAnalysisReproductionbio-chipseq-differential-binding🧬 bioinformatics (gptomics bio-* suite)bioinformatics — epigenomics & chromatindifferential

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-chipseq-differential-binding

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

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

R: DiffBind::dba() → dba.count() → dba.contrast() → dba.analyze().
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().
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).

Source Doc

Excerpt From SKILL.md

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.


## 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')