ATAC-seq Footprint Analysis FAQs

Question 1

When should I use this skill instead of standard ATAC-seq peak calling?

Accepted Answer

You should use this skill when you need high-resolution insights into which specific transcription factors are bound to DNA. While standard peak calling identifies open chromatin, footprinting identifies the 'shadows' left by TFs, allowing you to infer regulatory activity without performing multiple ChIP-seq experiments.

Question 2

Can this skill identify differential TF activity between samples?

Accepted Answer

Yes. By using the BINDetect feature, the skill can compare footprinting signals between different conditions (e.g., treated vs. control or different cell types) to identify which transcription factors are significantly gaining or losing binding activity.

Question 3

What does the ATAC-seq Footprint Analysis skill do?

Accepted Answer

This skill automates the TOBIAS (Transcription factor Occupancy prediction By Investigation of ATAC-seq Signal) framework. It performs Tn5 sequence bias correction, scans for transcription factor motifs, generates footprint scores, and detects differential TF binding across different experimental conditions.

Question 4

How does this skill improve my bioinformatics workflow?

Accepted Answer

It streamlines the complex multi-step TOBIAS pipeline within Claude Code. It handles the directory structure, validates required inputs like genome assemblies and PWMs, and executes sequential tasks—from bias correction (ATACorrect) to visualization (PlotAggregate)—minimizing manual command-line errors.

Question 5

What are the data requirements for successful footprinting?

Accepted Answer

For accurate results, it is recommended to have high-quality paired-end ATAC-seq data with at least 30 million reads per sample. You will need aligned BAM files (duplicate-removed), peak files in BED format, a genome FASTA file, and motif PWMs (typically from the JASPAR database).

ATAC-seq Footprint Analysis

ATAC-seq Footprint Analysis

Key Features

Use Cases

Key Features

Use Cases