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atac-footprinting

Identify transcription factor occupancy from ATAC-seq footprints using TOBIAS.

12|3|Updated Nov 4, 2025
Search Tags:#footprinting#atac-seq#differential-analysis#tobias#tf-binding#motif-scanning#bias-correction
One-click install
npx skills add https://github.com/BIsnake2001/ChromSkills --skill atac-footprinting
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: atac-footprinting
Source: https://github.com/BIsnake2001/ChromSkills/tree/main/28.ATACseq-footprint
Command: npx skills add https://github.com/BIsnake2001/ChromSkills --skill atac-footprinting

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyzing transcription factor occupancy from ATAC-seq data is challenging due to biases and the need to infer binding from accessibility patterns. This skill provides an end-to-end footprinting workflow using TOBIAS to correct Tn5 bias, scan motifs, score footprints, and optionally perform differential footprinting across conditions.

Core Features & Use Cases

  • Bias-corrected footprinting: Tn5 bias correction with ATACorrect to improve footprint accuracy.
  • Motif scanning & scoring: Identify motif sites and compute footprint scores across samples.
  • Differential footprinting: Compare TF occupancy between conditions and generate aggregated plots.
  • Outputs & reports: Provides TF-binding calls and plots for interpretation in downstream analyses.

Quick Start

Run the ATACseq footprinting workflow by supplying ATAC-seq BAMs, peak files, a reference genome, and motif PWMs to generate footprint scores and TF binding reports.

Frequently Asked Questions about atac-footprinting

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

FAQPage Schema
How do I identify transcription factor occupancy from ATAC-seq footprints?▼

You can identify transcription factor occupancy from ATAC-seq footprints by applying Tn5 bias correction, scanning motif sites, and computing footprint scores using TOBIAS to quantify TF binding activity across conditions.

How do I correct Tn5 bias in ATAC-seq footprinting analysis?▼

Tn5 bias correction in ATAC-seq footprinting is handled using the ATACorrect step, which adjusts raw cutsite signals to improve the accuracy of downstream transcription factor footprint scores.

Can I perform differential TF footprinting across multiple cell types with replicates?▼

Yes, differential footprinting compares TF occupancy between conditions or cell types using ATAC-seq datasets with replicates, generating aggregated plots and differential binding calls across samples.

What inputs do I need for ATAC-seq footprint scoring and motif scanning?▼

ATAC-seq footprint scoring and motif scanning require BAM files, peak calls, a reference genome, and motif PWMs to calculate binding scores and generate downstream TF occupancy reports.

Why does transcription factor motif scanning require bias correction before scoring footprints?▼

Transcription factor motif scanning requires bias correction because Tn5 transposase has sequence preference biases that distort accessibility signals, leading to inaccurate footprint scores if left uncorrected.

What is the best way to compare TF binding activity between ATAC-seq conditions?▼

The best way to compare TF binding activity between ATAC-seq conditions is to run a differential footprinting workflow that scores motif sites across samples and outputs aggregated plots and binding calls.

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