Agent skill

bio-hi-c-analysis-tad-detection

Call topologically associating domains (TADs) from Hi-C data using insulation score, HiCExplorer, and other methods. Identify domain boundaries and hierarchical domain structure. Use when calling TADs from Hi-C insulation scores.

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-hi-c-analysis-tad-detection

SKILL.md

Version Compatibility

Reference examples tested with: cooler 0.9+, cooltools 0.6+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+

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

  • Python: pip show <package> then help(module.function) to check signatures
  • 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.

TAD Detection

"Call TADs from my Hi-C data" → Identify topologically associating domain boundaries using insulation score minima or other boundary-detection algorithms.

  • Python: cooltools.insulation(clr, window_bp) then threshold boundary strength
  • CLI: hicFindTADs (HiCExplorer)

Call topologically associating domains from Hi-C contact matrices.

Required Imports

python
import cooler
import cooltools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import bioframe

Compute Insulation Score

python
clr = cooler.Cooler('matrix.mcool::resolutions/10000')
view_df = bioframe.make_viewframe(clr.chromsizes)

# Compute insulation score
insulation = cooltools.insulation(
    clr,
    window_bp=[100000, 200000, 500000],  # Multiple window sizes
    ignore_diags=2,
)

print(insulation.head())
# Columns include: chrom, start, end, log2_insulation_score_100000, etc.

Call TAD Boundaries

python
# Find boundaries (local minima in insulation score)
boundaries = cooltools.find_insulation(
    clr,
    window_bp=200000,  # Single window
    ignore_diags=2,
    min_dist_bad_bin=0,
)

# Filter significant boundaries
boundaries['is_boundary'] = boundaries['boundary_strength'] > 0.1
strong_boundaries = boundaries[boundaries['is_boundary']]
print(f'Found {len(strong_boundaries)} TAD boundaries')

Extract TAD Regions

Goal: Convert a set of TAD boundary positions into TAD interval coordinates (start-end pairs) for downstream overlap analysis.

Approach: Sort boundaries by position per chromosome, then define each TAD as the interval between consecutive boundary positions.

python
def boundaries_to_tads(boundaries_df, chrom):
    '''Convert boundary positions to TAD intervals'''
    chr_bounds = boundaries_df[
        (boundaries_df['chrom'] == chrom) &
        (boundaries_df['is_boundary'])
    ].sort_values('start')

    tads = []
    starts = [0] + list(chr_bounds['start'])
    ends = list(chr_bounds['start']) + [boundaries_df[boundaries_df['chrom'] == chrom]['end'].max()]

    for start, end in zip(starts, ends):
        if end > start:
            tads.append({'chrom': chrom, 'start': start, 'end': end})

    return pd.DataFrame(tads)

tads_chr1 = boundaries_to_tads(boundaries, 'chr1')
print(f'chr1 TADs: {len(tads_chr1)}')
print(tads_chr1.head())

Using HiCExplorer (CLI)

bash
# Compute TADs with HiCExplorer
hicFindTADs \
    -m matrix.cool \
    --outPrefix tads \
    --correctForMultipleTesting fdr \
    --minDepth 60000 \
    --maxDepth 200000 \
    --step 10000 \
    --thresholdComparisons 0.05

# Output files:
# tads_domains.bed - TAD intervals
# tads_boundaries.bed - Boundary positions
# tads_score.bedgraph - Insulation score track

Using HiCExplorer in Python

python
# After running hicFindTADs
tads = pd.read_csv('tads_domains.bed', sep='\t', header=None,
                   names=['chrom', 'start', 'end'])
boundaries = pd.read_csv('tads_boundaries.bed', sep='\t', header=None,
                         names=['chrom', 'start', 'end', 'score'])

print(f'TADs: {len(tads)}')
print(f'Boundaries: {len(boundaries)}')

TAD Statistics

python
# Calculate TAD sizes
tads['size'] = tads['end'] - tads['start']

print('TAD size statistics:')
print(f'  Mean: {tads["size"].mean() / 1000:.0f} kb')
print(f'  Median: {tads["size"].median() / 1000:.0f} kb')
print(f'  Min: {tads["size"].min() / 1000:.0f} kb')
print(f'  Max: {tads["size"].max() / 1000:.0f} kb')

# Size distribution
plt.hist(tads['size'] / 1000, bins=50)
plt.xlabel('TAD size (kb)')
plt.ylabel('Count')
plt.title('TAD size distribution')
plt.savefig('tad_sizes.png', dpi=150)

Plot Insulation Score

python
fig, ax = plt.subplots(figsize=(15, 3))

chr_data = insulation[insulation['chrom'] == 'chr1']
ax.plot(chr_data['start'] / 1e6, chr_data['log2_insulation_score_200000'])

# Mark boundaries
bounds = chr_data[chr_data['is_boundary']]
ax.scatter(bounds['start'] / 1e6, bounds['log2_insulation_score_200000'],
           color='red', s=20, zorder=5)

ax.set_xlabel('Position (Mb)')
ax.set_ylabel('Insulation score (log2)')
ax.set_title('chr1 insulation score (red = boundaries)')
plt.tight_layout()
plt.savefig('insulation_track.png', dpi=150)

Compare TAD Boundaries Between Conditions

python
# Load boundaries from two conditions
bounds1 = pd.read_csv('condition1_boundaries.bed', sep='\t',
                      names=['chrom', 'start', 'end'])
bounds2 = pd.read_csv('condition2_boundaries.bed', sep='\t',
                      names=['chrom', 'start', 'end'])

# Find overlapping boundaries (within tolerance)
tolerance = 50000  # 50kb

def find_overlaps(df1, df2, tol):
    overlaps = []
    for _, b1 in df1.iterrows():
        matches = df2[
            (df2['chrom'] == b1['chrom']) &
            (abs(df2['start'] - b1['start']) <= tol)
        ]
        if len(matches) > 0:
            overlaps.append(b1)
    return pd.DataFrame(overlaps)

shared = find_overlaps(bounds1, bounds2, tolerance)
print(f'Shared boundaries: {len(shared)}')
print(f'Condition 1 specific: {len(bounds1) - len(shared)}')
print(f'Condition 2 specific: {len(bounds2) - len(shared)}')

Hierarchical TADs

python
# Compute insulation at multiple scales
windows = [100000, 200000, 500000, 1000000]
insulation_multi = cooltools.insulation(clr, window_bp=windows, ignore_diags=2)

# Boundaries at each scale represent different hierarchy levels
for w in windows:
    col = f'is_boundary_{w}'
    n_bounds = insulation_multi[col].sum()
    print(f'Window {w/1000:.0f}kb: {n_bounds} boundaries')

Export TADs

python
# Save as BED
tads[['chrom', 'start', 'end']].to_csv(
    'tads.bed', sep='\t', index=False, header=False
)

# Save boundaries as BED
boundaries[boundaries['is_boundary']][['chrom', 'start', 'end', 'boundary_strength']].to_csv(
    'boundaries.bed', sep='\t', index=False, header=False
)

# Save insulation as bedGraph
insulation[['chrom', 'start', 'end', 'log2_insulation_score_200000']].to_csv(
    'insulation.bedgraph', sep='\t', index=False, header=False
)

Related Skills

  • hic-data-io - Load Hi-C matrices
  • hic-visualization - Visualize TADs on contact matrices
  • compartment-analysis - Compartments operate at larger scale than TADs

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