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BigWig Tracks

BigWig is an indexed binary format for continuous genomic data. Efficient for genome browsers and programmatic access.

Why BigWig?

| Format | Size | Random Access | Browser Support | |--------|------|---------------|-----------------| | bedGraph | Large | No | Limited | | bigWig | ~10x smaller | Yes (indexed) | Excellent |

Convert bedGraph to bigWig (CLI)

Installation

bash
# UCSC tools
conda install -c bioconda ucsc-bedgraphtobigwig ucsc-bigwigtobedgraph

# Or download directly
wget http://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig
chmod +x bedGraphToBigWig

Basic Conversion

bash
# Sort bedGraph first (required)
sort -k1,1 -k2,2n coverage.bedGraph > coverage.sorted.bedGraph

# Convert to bigWig
bedGraphToBigWig coverage.sorted.bedGraph chrom.sizes output.bw

# chrom.sizes format: chr<TAB>size
# chr1	248956422
# chr2	242193529

Get Chromosome Sizes

bash
# From FASTA index
cut -f1,2 reference.fa.fai > chrom.sizes

# Download from UCSC
wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes

# From BAM header
samtools view -H alignments.bam | grep @SQ | sed 's/@SQ\tSN:\|LN://g' > chrom.sizes

Full Workflow

bash
# Generate bedGraph from BAM
bedtools genomecov -ibam alignments.bam -bg > coverage.bedGraph

# Sort bedGraph
sort -k1,1 -k2,2n coverage.bedGraph > coverage.sorted.bedGraph

# Convert to bigWig
bedGraphToBigWig coverage.sorted.bedGraph hg38.chrom.sizes coverage.bw

# Clean up intermediate files
rm coverage.bedGraph coverage.sorted.bedGraph

Read BigWig with pyBigWig (Python)

Installation

bash
pip install pyBigWig

Open and Inspect

python
import pyBigWig

# Open file
bw = pyBigWig.open('coverage.bw')

# File info
print(f'Chromosomes: {bw.chroms()}')
print(f'Header: {bw.header()}')

# Check if file is bigWig (not bigBed)
print(f'Is bigWig: {bw.isBigWig()}')

# Close when done
bw.close()

Extract Values

python
import pyBigWig

bw = pyBigWig.open('coverage.bw')

# Get values for a region (returns numpy array)
values = bw.values('chr1', 1000000, 1001000)
print(f'Mean: {values.mean():.2f}')
print(f'Max: {values.max():.2f}')

# Get specific intervals with values
intervals = bw.intervals('chr1', 1000000, 1001000)
# Returns: [(start, end, value), ...]
for start, end, val in intervals:
    print(f'{start}-{end}: {val}')

# Statistics for region
stats = bw.stats('chr1', 1000000, 1001000, type='mean')
print(f'Mean coverage: {stats[0]:.2f}')

# Available stat types: mean, min, max, coverage, std, sum
max_val = bw.stats('chr1', 1000000, 1001000, type='max')
coverage = bw.stats('chr1', 1000000, 1001000, type='coverage')

bw.close()

Binned Statistics

python
import pyBigWig

bw = pyBigWig.open('coverage.bw')

# Get mean values in 100bp bins across region
region_start, region_end = 1000000, 2000000
n_bins = 1000  # 100bp bins

binned = bw.stats('chr1', region_start, region_end, type='mean', nBins=n_bins)
# Returns list of n_bins values

bw.close()

Extract for BED Regions

python
import pyBigWig
import pybedtools

bw = pyBigWig.open('coverage.bw')
bed = pybedtools.BedTool('regions.bed')

# Get mean signal per region
results = []
for interval in bed:
    chrom, start, end = interval.chrom, interval.start, interval.end
    mean_signal = bw.stats(chrom, start, end, type='mean')[0]
    results.append({
        'chrom': chrom,
        'start': start,
        'end': end,
        'name': interval.name,
        'signal': mean_signal if mean_signal else 0
    })

bw.close()

# Convert to DataFrame
import pandas as pd
df = pd.DataFrame(results)
print(df)

Create BigWig with pyBigWig

python
import pyBigWig

# Create new bigWig
bw = pyBigWig.open('output.bw', 'w')

# Add header (chromosome sizes)
bw.addHeader([('chr1', 248956422), ('chr2', 242193529)])

# Add entries (must be sorted by position)
# Method 1: Individual entries
bw.addEntries(['chr1', 'chr1'], [0, 100], ends=[100, 200], values=[1.5, 2.3])

# Method 2: Chromosome at a time (more efficient)
bw.addEntries('chr1', [0, 100, 200], ends=[100, 200, 300], values=[1.5, 2.3, 3.1])

# Method 3: Fixed-width spans (most efficient for dense data)
bw.addEntries('chr2', 0, values=[1.0, 2.0, 3.0, 4.0], span=100, step=100)
# Creates: chr2:0-100=1.0, chr2:100-200=2.0, chr2:200-300=3.0, chr2:300-400=4.0

bw.close()

deepTools for BigWig Operations

Installation

bash
conda install -c bioconda deeptools

Generate Normalized BigWig from BAM

bash
# RPKM normalization
bamCoverage -b alignments.bam -o coverage.bw --normalizeUsing RPKM

# CPM normalization
bamCoverage -b alignments.bam -o coverage.bw --normalizeUsing CPM

# BPM (bins per million) - like TPM for ChIP-seq
bamCoverage -b alignments.bam -o coverage.bw --normalizeUsing BPM

# With bin size and smoothing
bamCoverage -b alignments.bam -o coverage.bw \
    --binSize 10 \
    --normalizeUsing CPM \
    --smoothLength 30

# Extend reads to fragment length
bamCoverage -b alignments.bam -o coverage.bw \
    --extendReads 200 \
    --normalizeUsing CPM

Compare BigWig Files

bash
# Log2 ratio of two bigWig files
bigwigCompare -b1 treatment.bw -b2 control.bw -o log2ratio.bw --ratio log2

# Subtract
bigwigCompare -b1 treatment.bw -b2 control.bw -o diff.bw --ratio subtract

# Mean of multiple files
bigwigAverage -b file1.bw file2.bw file3.bw -o average.bw

Summarize BigWig Over Regions

bash
# Matrix for heatmap (signal around regions)
computeMatrix reference-point -S signal.bw -R regions.bed \
    -b 2000 -a 2000 -o matrix.gz

# Plot heatmap
plotHeatmap -m matrix.gz -o heatmap.png

# Summary statistics per region
multiBigwigSummary BED-file -b sample1.bw sample2.bw -o results.npz --BED regions.bed

Convert BigWig to bedGraph

bash
# Using UCSC tool
bigWigToBedGraph input.bw output.bedGraph

# Extract specific region
bigWigToBedGraph input.bw output.bedGraph -chrom=chr1 -start=1000000 -end=2000000

Common Patterns

ChIP-seq Signal Track

bash
# Generate normalized track
bamCoverage -b chip.bam -o chip.bw \
    --normalizeUsing CPM \
    --extendReads 200 \
    --binSize 10

# Generate input-subtracted track
bigwigCompare -b1 chip.bw -b2 input.bw -o chip_minus_input.bw --ratio subtract

RNA-seq Coverage

bash
# Strand-specific coverage
bamCoverage -b rnaseq.bam -o forward.bw --filterRNAstrand forward
bamCoverage -b rnaseq.bam -o reverse.bw --filterRNAstrand reverse

Extract Signal for Analysis

python
import pyBigWig
import pandas as pd

def extract_signal(bw_path, bed_path, stat='mean'):
    '''Extract bigWig signal for BED regions.'''
    import pybedtools
    bw = pyBigWig.open(bw_path)
    bed = pybedtools.BedTool(bed_path)

    results = []
    for interval in bed:
        val = bw.stats(interval.chrom, interval.start, interval.end, type=stat)[0]
        results.append({
            'chrom': interval.chrom,
            'start': interval.start,
            'end': interval.end,
            'name': interval.name if interval.name else '.',
            'signal': val if val is not None else 0
        })

    bw.close()
    return pd.DataFrame(results)

# Usage
df = extract_signal('coverage.bw', 'peaks.bed', stat='mean')
print(df)

Related Skills

• coverage-analysis - Generate bedGraph input
• chip-seq/chipseq-visualization - ChIP-seq signal tracks
• alignment-files/bam-statistics - BAM to coverage
• interval-arithmetic - Region operations

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