TF Footprinting

TOBIAS Workflow

# 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

# 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

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