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single-cell-atac-seq-peak-calling-annotaion

Call ATAC-seq peaks with MACS2, annotate genomic features, and detect differential accessibility with DESeq2.

1.1k|132|Updated Apr 13, 2023
Search Tags:#genomics#atac-seq#peak calling#chipseeker#macs2#chromatin accessibility#deSeq2
One-click install
npx skills add https://github.com/PharMolix/OpenBioMed --skill single-cell-atac-seq-peak-calling-annotaion
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: single-cell-atac-seq-peak-calling-annotaion
Source: https://github.com/PharMolix/OpenBioMed/tree/main/skills/single-cell-atac-seq-peak-calling-annotaion
Command: npx skills add https://github.com/PharMolix/OpenBioMed --skill single-cell-atac-seq-peak-calling-annotaion

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Bulk ATAC-seq analysis requires ATAC-specific peak calling parameters, blacklist filtering, consensus peak construction, and pseudobulk statistics that generic workflows get wrong, leading to artifactual peaks and invalid differential accessibility results.

Core Features & Use Cases

  • ATAC-seq-specific peak calling: Runs MACS2 with --nomodel --shift -100 --extsize 200 --format BAMPE and filters ENCODE blacklist regions.
  • Consensus peaks and annotation: Merges per-sample peaks with bedtools into a consensus set and annotates promoter, UTR, exon, intron, and intergenic features using ChIPseeker.
  • Differential accessibility analysis: Builds a count matrix and runs pseudobulk DESeq2 with FDR correction, producing ranked DAR tables and volcano plots.
  • Use Case: Compare chromatin accessibility between three disease and three control samples to identify promoter and enhancer regions that open or close in disease, such as a MKI67 promoter gaining accessibility.

Quick Start

Call peaks from my aligned ATAC-seq BAM files, annotate them against hg38, and find differentially accessible regions between my disease and control samples.

Frequently Asked Questions about single-cell-atac-seq-peak-calling-annotaion

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I call peaks from ATAC-seq data with MACS2?▼

Run macs2 callpeak with -f BAMPE, --nomodel, --shift -100, --extsize 200, and -p 0.01 on your aligned BAM files. These flags center signal on Tn5 cut sites and skip the ChIP-seq enrichment model that does not fit ATAC-seq data.

How to find differentially accessible regions between ATAC-seq samples?▼

Build a consensus peak set with bedtools merge, count reads per sample per peak, then run DESeq2 on the pseudobulk count matrix with your condition as the design variable. Filter significant DARs at FDR < 0.05 and |log2FC| > 1.

Why should ATAC-seq peaks be filtered against ENCODE blacklist regions?▼

Blacklist regions contain repetitive elements that produce artifactual signal regardless of experimental condition. Removing them with bedtools intersect -v prevents false peaks that would otherwise inflate your peak counts and distort differential analysis.

Should I use IDR or bedtools merge for consensus ATAC-seq peaks?▼

IDR is more stringent but requires exactly two replicates per condition. For three or more replicates or multi-condition designs, bedtools merge with a 100 bp distance threshold is more practical and produces a reproducible consensus peak universe.

How many consensus peaks are normal for bulk ATAC-seq?▼

Between 50,000 and 150,000 consensus peaks is typical for human cell types. Fewer than 20,000 suggests low library complexity or failed peak calling, while more than 300,000 may indicate a too-permissive p-value threshold.

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