bio-atac-seq-footprinting

name	bio-atac-seq-footprinting
description	Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting.
tool_type	cli
primary_tool	tobias

Version Compatibility

Reference examples tested with: bedtools 2.31+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, pyBigWig 0.3+, samtools 1.19+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

TF Footprinting

"Identify TF binding footprints in my ATAC-seq data" → Detect protected DNA regions within accessible chromatin where bound transcription factors block Tn5 insertion.

• CLI: TOBIAS ATACorrect → TOBIAS FootprintScores → TOBIAS BINDetect

TOBIAS Workflow

Goal: Identify transcription factor binding footprints within accessible chromatin regions.

Approach: Correct Tn5 insertion bias, compute per-base footprint scores, then detect bound/unbound TF motif sites using the three-step TOBIAS pipeline.

# 1. Correct Tn5 bias
tobias ATACorrect \
    --bam sample.bam \
    --genome genome.fa \
    --peaks peaks.bed \
    --outdir corrected/ \
    --cores 8

# 2. Calculate footprint scores
tobias FootprintScores \
    --signal corrected/sample_corrected.bw \
    --regions peaks.bed \
    --output footprints.bw \
    --cores 8

# 3. Bind TF motifs
tobias BINDetect \
    --motifs JASPAR_motifs.pfm \
    --signals footprints.bw \
    --genome genome.fa \
    --peaks peaks.bed \
    --outdir bindetect_output/ \
    --cores 8

TOBIAS Differential Footprinting

Goal: Compare TF binding between two conditions to identify regulators with differential activity.

Approach: Provide two bias-corrected signal tracks to BINDetect, which scores each motif site for differential binding between conditions.

# Compare conditions
tobias BINDetect \
    --motifs JASPAR_motifs.pfm \
    --signals condition1.bw condition2.bw \
    --genome genome.fa \
    --peaks consensus_peaks.bed \
    --outdir differential_footprints/ \
    --cond_names condition1 condition2 \
    --cores 8

# Output includes:
# - Differential binding scores
# - Per-TF statistics
# - Bound/unbound site predictions

Download JASPAR Motifs

# Download JASPAR motifs
wget https://jaspar.genereg.net/download/data/2022/CORE/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt
mv JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt JASPAR_motifs.pfm

Prepare Input Files

# Ensure BAM is sorted and indexed
samtools sort -@ 8 sample.bam -o sample.sorted.bam
samtools index sample.sorted.bam

# Filter peaks (remove blacklist, size filter)
bedtools intersect -v -a peaks.narrowPeak -b blacklist.bed | \
    awk '$3-$2 >= 100 && $3-$2 <= 5000' > filtered_peaks.bed

HINT-ATAC Alternative

# RGT suite HINT-ATAC
rgt-hint footprinting \
    --atac-seq \
    --organism hg38 \
    --output-prefix sample \
    sample.bam peaks.bed

PIQ Footprinting

# PIQ (another footprinting tool)
library(PIQ)

# Load data
bam <- 'sample.bam'
pwms <- readMotifs('JASPAR_motifs.pfm')

# Run footprinting
piq_results <- piq(bam, pwms, genome='hg38')

Aggregate Footprint Plots

# TOBIAS PlotAggregate
tobias PlotAggregate \
    --TFBS bindetect_output/*/beds/*_bound.bed \
    --signals corrected/sample_corrected.bw \
    --output aggregate_footprints.pdf \
    --share_y \
    --plot_boundaries

Python: Custom Footprint Analysis

Goal: Extract and visualize aggregate ATAC-seq signal around predicted TF binding sites.

Approach: Sample bigWig signal values in windows centered on motif sites, average across all sites, and plot the characteristic V-shaped footprint.

import pyBigWig
import numpy as np
import pandas as pd
from pyfaidx import Fasta

def extract_footprint_signal(bigwig_file, bed_file, flank=100):
    '''Extract signal around binding sites.'''
    bw = pyBigWig.open(bigwig_file)

    signals = []
    for line in open(bed_file):
        fields = line.strip().split('\t')
        chrom, start, end = fields[0], int(fields[1]), int(fields[2])
        center = (start + end) // 2

        try:
            vals = bw.values(chrom, center - flank, center + flank)
            if vals:
                signals.append(vals)
        except:
            continue

    avg_signal = np.nanmean(signals, axis=0)
    return avg_signal

def plot_footprint(signal, output_file):
    '''Plot aggregate footprint.'''
    import matplotlib.pyplot as plt

    x = np.arange(-len(signal)//2, len(signal)//2)

    plt.figure(figsize=(8, 4))
    plt.plot(x, signal, 'b-', linewidth=2)
    plt.axvline(0, color='red', linestyle='--', alpha=0.5)
    plt.xlabel('Distance from motif center (bp)')
    plt.ylabel('ATAC-seq signal')
    plt.title('Aggregate Footprint')
    plt.savefig(output_file, dpi=150)
    plt.close()

Scan for Motifs

# Find motif occurrences in peaks
# Using FIMO (MEME suite)
fimo --oc fimo_output motifs.meme peaks.fa

# Or HOMER
findMotifsGenome.pl peaks.bed hg38 motif_analysis/ -find motif.motif

Interpret Footprint Depth

Footprint Depth	Interpretation
Deep footprint	Strong TF binding
Shallow footprint	Weak/transient binding
No footprint	No binding or wrong motif
Shoulders only	Nucleosome positioning

Quality Considerations

# Footprinting requires:
# - High read depth (>50M reads)
# - NFR-enriched signal (filter for <100bp fragments)
# - Good Tn5 bias correction

# Extract NFR reads
samtools view -h sample.bam | \
    awk 'substr($0,1,1)=="@" || ($9>0 && $9<100) || ($9<0 && $9>-100)' | \
    samtools view -b > nfr.bam

Differential TF Activity

def compare_footprints(tf_name, cond1_bw, cond2_bw, motif_bed):
    '''Compare TF footprints between conditions.'''
    sig1 = extract_footprint_signal(cond1_bw, motif_bed)
    sig2 = extract_footprint_signal(cond2_bw, motif_bed)

    # Calculate footprint depth
    depth1 = np.nanmean(sig1[:30]) - np.nanmin(sig1[40:60])
    depth2 = np.nanmean(sig2[:30]) - np.nanmin(sig2[40:60])

    diff = depth2 - depth1

    return {
        'TF': tf_name,
        'depth_cond1': depth1,
        'depth_cond2': depth2,
        'difference': diff
    }

TOBIAS Output Files

File	Description
*_corrected.bw	Bias-corrected signal
*_footprints.bw	Footprint scores
*_bound.bed	Predicted bound sites
*_unbound.bed	Predicted unbound sites
*_overview.txt	Per-TF statistics

Related Skills

• atac-seq/atac-peak-calling - Generate peaks
• atac-seq/atac-qc - Verify data quality
• chip-seq/peak-annotation - Annotate binding sites
• sequence-manipulation/motif-search - Find motifs