bio-atac-seq-footprinting

1

FreedomIntelligence/OpenClaw-Medical-SkillsBiology Medicine and BioinformaticsReview advisedSnapshot 38dc472b2d02

Installs: 0

Summary

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.

SKILL.md

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

# 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
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

# 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

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

# 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')

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

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()

# 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

# 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

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
    }