Coverage Analysis with Uniwig

Coverage Analysis with Uniwig

The uniwig module generates coverage tracks from sequencing data, providing efficient conversion of genomic intervals to coverage profiles.

Coverage Track Generation

Create coverage tracks from BED files:

import gtars

# Generate coverage from BED file
coverage = gtars.uniwig.coverage_from_bed("fragments.bed")

# Generate coverage with specific resolution
coverage = gtars.uniwig.coverage_from_bed("fragments.bed", resolution=10)

# Generate strand-specific coverage
fwd_coverage = gtars.uniwig.coverage_from_bed("fragments.bed", strand="+")
rev_coverage = gtars.uniwig.coverage_from_bed("fragments.bed", strand="-")

CLI Usage

Generate coverage tracks from command line:

# Generate coverage track
gtars uniwig generate --input fragments.bed --output coverage.wig

# Specify resolution
gtars uniwig generate --input fragments.bed --output coverage.wig --resolution 10

# Generate BigWig format
gtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig

# Strand-specific coverage
gtars uniwig generate --input fragments.bed --output forward.wig --strand +
gtars uniwig generate --input fragments.bed --output reverse.wig --strand -

Working with Coverage Data

Accessing Coverage Values

Query coverage at specific positions:

# Get coverage at position
cov = coverage.get_coverage("chr1", 1000)

# Get coverage over range
cov_array = coverage.get_coverage_range("chr1", 1000, 2000)

# Get coverage statistics
mean_cov = coverage.mean_coverage("chr1", 1000, 2000)
max_cov = coverage.max_coverage("chr1", 1000, 2000)

Coverage Operations

Perform operations on coverage tracks:

# Normalize coverage
normalized = coverage.normalize()

# Smooth coverage
smoothed = coverage.smooth(window_size=10)

# Combine coverage tracks
combined = coverage1.add(coverage2)

# Compute coverage difference
diff = coverage1.subtract(coverage2)

Output Formats

Uniwig supports multiple output formats:

WIG Format

Standard wiggle format:

fixedStep chrom=chr1 start=1000 step=1
12
15
18
22
...

BigWig Format

Binary format for efficient storage and access:

# Generate BigWig
gtars uniwig generate --input fragments.bed --output coverage.bw --format bigwig

BedGraph Format

Flexible format for variable coverage:

chr1    1000    1001    12
chr1    1001    1002    15
chr1    1002    1003    18

Use Cases

ATAC-seq Analysis

Generate chromatin accessibility profiles:

# Generate ATAC-seq coverage
atac_fragments = gtars.RegionSet.from_bed("atac_fragments.bed")
coverage = gtars.uniwig.coverage_from_bed("atac_fragments.bed", resolution=1)

# Identify accessible regions
peaks = coverage.call_peaks(threshold=10)

ChIP-seq Peak Visualization

Create coverage tracks for ChIP-seq data:

# Generate coverage for visualization
gtars uniwig generate --input chip_seq_fragments.bed \
                      --output chip_coverage.bw \
                      --format bigwig

RNA-seq Coverage

Compute read coverage for RNA-seq:

# Generate strand-specific RNA-seq coverage
fwd = gtars.uniwig.coverage_from_bed("rnaseq.bed", strand="+")
rev = gtars.uniwig.coverage_from_bed("rnaseq.bed", strand="-")

# Export for IGV
fwd.to_bigwig("rnaseq_fwd.bw")
rev.to_bigwig("rnaseq_rev.bw")

Differential Coverage Analysis

Compare coverage between samples:

# Generate coverage for two samples
control = gtars.uniwig.coverage_from_bed("control.bed")
treatment = gtars.uniwig.coverage_from_bed("treatment.bed")

# Compute fold change
fold_change = treatment.divide(control)

# Find differential regions
diff_regions = fold_change.find_regions(threshold=2.0)

Performance Optimization

• Use appropriate resolution for data scale
• BigWig format recommended for large datasets
• Parallel processing available for multiple chromosomes
• Memory-efficient streaming for large files