bio-hi-c-analysis-loop-calling

name	bio-hi-c-analysis-loop-calling
description	Detect chromatin loops and point interactions from Hi-C data using cooltools, chromosight, and HiCCUPS-like methods. Identify CTCF-mediated loops and enhancer-promoter contacts. Use when detecting chromatin loops from Hi-C data.
tool_type	mixed
primary_tool	cooltools

Version Compatibility

Reference examples tested with: bedtools 2.31+, cooler 0.9+, cooltools 0.6+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, pybedtools 0.9+

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.

Chromatin Loop Calling

"Call chromatin loops from my Hi-C data" → Detect point enrichments in contact matrices representing CTCF-mediated loops and enhancer-promoter interactions.

• Python: cooltools.dots() or chromosight detect --pattern=loops

Detect chromatin loops and point interactions from Hi-C data.

Required Imports

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

Call Loops with cooltools (Dot Calling)

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

# Compute expected values
expected = cooltools.expected_cis(clr, view_df=view_df, ignore_diags=2)

# Call dots (loops)
dots = cooltools.dots(
    clr,
    expected=expected,
    view_df=view_df,
    max_loci_separation=2000000,  # Max loop size (2Mb)
    max_nans_tolerated=0.5,
)

print(f'Found {len(dots)} loops')
print(dots.head())

Using chromosight (CLI)

# Call loops with chromosight
chromosight detect \
    --pattern loops \
    --min-dist 20000 \
    --max-dist 2000000 \
    matrix.cool \
    loops_output

# Output: loops_output.tsv with loop coordinates and scores

Parse chromosight Output

loops = pd.read_csv('loops_output.tsv', sep='\t')
print(f'Found {len(loops)} loops')
print(loops.head())

# Columns: chrom1, start1, end1, chrom2, start2, end2, score, etc.

Using HiCExplorer hicDetectLoops

# Call loops with HiCExplorer
hicDetectLoops \
    -m matrix.cool \
    -o loops.bedgraph \
    --maxLoopDistance 2000000 \
    --windowSize 10 \
    --peakWidth 6 \
    --pValuePreselection 0.05 \
    --pValue 0.05

Loop Statistics

# Calculate loop sizes
loops['size'] = abs(loops['end2'] - loops['start1'])

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

# Size distribution
plt.hist(loops['size'] / 1000, bins=50)
plt.xlabel('Loop size (kb)')
plt.ylabel('Count')
plt.savefig('loop_sizes.png', dpi=150)

Filter Loops by Score

# Keep high-confidence loops
score_threshold = loops['score'].quantile(0.75)
high_conf_loops = loops[loops['score'] >= score_threshold]
print(f'High confidence loops: {len(high_conf_loops)}')

Annotate Loops with Features

import pybedtools

# Convert loop anchors to BED
anchor1 = loops[['chrom1', 'start1', 'end1']].copy()
anchor1.columns = ['chrom', 'start', 'end']
anchor2 = loops[['chrom2', 'start2', 'end2']].copy()
anchor2.columns = ['chrom', 'start', 'end']

# Load CTCF peaks
ctcf_peaks = pybedtools.BedTool('ctcf_peaks.bed')

# Intersect anchors with CTCF
anchor1_bed = pybedtools.BedTool.from_dataframe(anchor1)
anchor1_ctcf = anchor1_bed.intersect(ctcf_peaks, wa=True, u=True)

print(f'Anchors with CTCF: {len(anchor1_ctcf)} / {len(anchor1)}')

Compare Loops Between Conditions

# Load loops from two conditions
loops1 = pd.read_csv('condition1_loops.bedpe', sep='\t')
loops2 = pd.read_csv('condition2_loops.bedpe', sep='\t')

# Find overlapping loops
tolerance = 20000  # 20kb

def loops_overlap(l1, l2, tol):
    return (l1['chrom1'] == l2['chrom1'] and
            l1['chrom2'] == l2['chrom2'] and
            abs(l1['start1'] - l2['start1']) <= tol and
            abs(l1['start2'] - l2['start2']) <= tol)

shared = []
for _, loop1 in loops1.iterrows():
    for _, loop2 in loops2.iterrows():
        if loops_overlap(loop1, loop2, tolerance):
            shared.append(loop1)
            break

print(f'Shared loops: {len(shared)}')
print(f'Condition 1 specific: {len(loops1) - len(shared)}')
print(f'Condition 2 specific: {len(loops2) - len(set(range(len(loops2))) - set([]))}')

Aggregate Peak Analysis (APA)

Goal: Assess the overall strength and validity of called loops by stacking contact sub-matrices centered on loop anchors and averaging the signal.

Approach: For each loop, extract a fixed-size snippet from the contact matrix centered on the loop anchor pair, then compute the element-wise mean across all snippets to produce an aggregate enrichment map.

# Stack loops and compute average signal
from cooltools.lib import snip

def compute_apa(clr, loops, window=100000, resolution=10000):
    '''Compute average peak analysis'''
    flank = window // resolution

    stacks = []
    for _, loop in loops.iterrows():
        try:
            # Get region around loop
            snippet = clr.matrix(balance=True).fetch(
                f"{loop['chrom1']}:{loop['start1']-window}-{loop['end1']+window}",
                f"{loop['chrom2']}:{loop['start2']-window}-{loop['end2']+window}"
            )
            if snippet.shape[0] == snippet.shape[1]:
                stacks.append(snippet)
        except:
            continue

    if len(stacks) > 0:
        apa = np.nanmean(stacks, axis=0)
        return apa
    return None

apa_matrix = compute_apa(clr, loops.head(100))
if apa_matrix is not None:
    plt.imshow(np.log2(apa_matrix), cmap='Reds')
    plt.colorbar(label='log2(contact)')
    plt.title()
    plt.savefig(, dpi=)

Using cooltools pileup for APA

import cooltools

# Compute pileup (APA)
stack = cooltools.pileup(
    clr,
    features=loops[['chrom1', 'start1', 'end1', 'chrom2', 'start2', 'end2']],
    view_df=view_df,
    expected=expected,
    flank=100000,
)

# Average across all features
apa = np.nanmean(stack, axis=2)

Export Loops

# Save as BEDPE
loops[['chrom1', 'start1', 'end1', 'chrom2', 'start2', 'end2', 'score']].to_csv(
    'loops.bedpe', sep='\t', index=False, header=False
)

# Save as Juicer format (for visualization in Juicebox)
loops_juicer = loops.copy()
loops_juicer['color'] = '0,0,255'  # Blue
loops_juicer[['chrom1', 'start1', 'end1', 'chrom2', 'start2', 'end2', 'color']].to_csv(
    'loops.2dbed', sep='\t', index=False, header=False
)

Loops at Promoter-Enhancer Pairs

# Check if loops connect promoters and enhancers
promoters = pd.read_csv('promoters.bed', sep='\t', names=['chrom', 'start', 'end', 'gene'])
enhancers = pd.read_csv('enhancers.bed', sep='\t', names=['chrom', 'start', 'end'])

# For each loop, check if one anchor is promoter, other is enhancer
pe_loops = []
for _, loop in loops.iterrows():
    # Check anchor 1
    anchor1_prom = any((promoters['chrom'] == loop['chrom1']) &
                       (promoters['start'] <= loop['end1']) &
                       (promoters['end'] >= loop['start1']))
    anchor1_enh = any((enhancers['chrom'] == loop['chrom1']) &
                      (enhancers['start'] <= loop['end1']) &
                      (enhancers['end'] >= loop['start1']))

    # Check anchor 2
    anchor2_prom = any((promoters['chrom'] == loop['chrom2']) &
                       (promoters['start'] <= loop['end2']) &
                       (promoters['end'] >= loop['start2']))
    anchor2_enh = any((enhancers['chrom'] == loop['chrom2']) &
                      (enhancers['start'] <= loop['end2']) &
                      (enhancers['end'] >= loop[]))

     (anchor1_prom  anchor2_enh)  (anchor1_enh  anchor2_prom):
        pe_loops.append(loop)

()

Related Skills

• hic-data-io - Load Hi-C matrices
• hic-visualization - Visualize loops
• chip-seq - CTCF ChIP-seq for loop anchor validation