--- name: bio-genome-intervals-bigwig-tracks source: https://app.decimal.ai/s/bio-genome-intervals-bigwig-tracks@2/SKILL.md source_sha256: 668a95480c31 --- # 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: chrsize # 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