FridrichMethod 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
# 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
# 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
# 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
# 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
pip install pyBigWig
Open and Inspect
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
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
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
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
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
conda install -c bioconda deeptools
Generate Normalized BigWig from BAM
# 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
# 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
# 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
# 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
# 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
# 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
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