bio-chipseq-visualization

name: bio-chipseq-visualization description: Visualize ChIP-seq data using deepTools, Gviz, and ChIPseeker. Create heatmaps, profile plots, and genome browser tracks. Visualize signal around peaks, TSS, or custom regions. Use when visualizing ChIP-seq signal and peaks. tool_type: mixed primary_tool: deepTools measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command

ChIP-seq Visualization

deepTools - Compute Matrix

# Compute signal matrix around TSS
computeMatrix reference-point \
    --referencePoint TSS \
    -b 3000 -a 3000 \              # 3kb upstream and downstream
    -R genes.bed \                  # Reference regions
    -S sample.bw \                  # Signal file (bigWig)
    -o matrix.gz \
    --outFileSortedRegions sorted_genes.bed

deepTools - Scale-Regions

# Signal across gene bodies
computeMatrix scale-regions \
    -R genes.bed \
    -S sample1.bw sample2.bw \
    -b 3000 -a 3000 \              # Flanking regions
    -m 5000 \                       # Scaled body length
    -o matrix_scaled.gz

deepTools - Heatmap

# Generate heatmap from matrix
plotHeatmap \
    -m matrix.gz \
    -o heatmap.png \
    --colorMap RdBu \
    --whatToShow 'heatmap and colorbar' \
    --zMin -3 --zMax 3

# With profile on top
plotHeatmap \
    -m matrix.gz \
    -o heatmap_with_profile.png \
    --plotTitle 'H3K4me3 Signal' \
    --heatmapHeight 15 \
    --refPointLabel TSS

deepTools - Profile Plot

# Average profile plot
plotProfile \
    -m matrix.gz \
    -o profile.png \
    --plotTitle 'Average Signal Profile' \
    --perGroup

# Multiple samples comparison
plotProfile \
    -m matrix_multi.gz \
    -o profile_compare.png \
    --colors red blue green \
    --plotTitle 'Sample Comparison'

Create BigWig from BAM

# Normalized bigWig (CPM)
bamCoverage \
    -b sample.bam \
    -o sample.bw \
    --normalizeUsing CPM \
    --binSize 10 \
    --numberOfProcessors 8

# With input subtraction
bamCompare \
    -b1 chip.bam \
    -b2 input.bam \
    -o chip_vs_input.bw \
    --operation log2ratio \
    --binSize 50

ChIPseeker Profile Heatmap (R)

library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene

# Load peaks
peaks <- readPeakFile('sample_peaks.narrowPeak')

# Get promoter regions
promoter <- getPromoters(TxDb = txdb, upstream = 3000, downstream = 3000)

# Compute tag matrix
tagMatrix <- getTagMatrix(peaks, windows = promoter)

# Heatmap
tagHeatmap(tagMatrix, xlim = c(-3000, 3000), color = 'red')

# Profile plot
plotAvgProf(tagMatrix, xlim = c(-3000, 3000), xlab = 'Distance from TSS (bp)',
            ylab = 'Peak Count Frequency')

# With confidence interval
plotAvgProf2(tagMatrix, xlim = c(-3000, 3000), conf = 0.95)

Gviz - Genome Browser Tracks (R)

library(Gviz)
library(GenomicRanges)

# Define region
chr <- 'chr1'
start <- 1000000
end <- 1100000

# Ideogram track
itrack <- IdeogramTrack(genome = 'hg38', chromosome = chr)

# Genome axis
gtrack <- GenomeAxisTrack()

# Data track from bigWig
dtrack <- DataTrack(
    range = 'sample.bw',
    genome = 'hg38',
    type = 'histogram',
    name = 'ChIP Signal',
    col.histogram = 'darkblue',
    fill.histogram = 'darkblue'
)

# Gene track
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
grtrack <- GeneRegionTrack(txdb, genome = 'hg38', chromosome = chr, name = 'Genes')

# Plot
plotTracks(list(itrack, gtrack, dtrack, grtrack),
           from = start, to = end, chromosome = chr)

Multiple Samples in Gviz

# Create data tracks for each sample
dtrack1 <- DataTrack(range = 'control.bw', genome = 'hg38', name = 'Control',
                      type = 'histogram', col.histogram = 'blue', fill.histogram = 'blue')
dtrack2 <- DataTrack(range = 'treatment.bw', genome = 'hg38', name = 'Treatment',
                      type = 'histogram', col.histogram = 'red', fill.histogram = 'red')

plotTracks(list(itrack, gtrack, dtrack1, dtrack2, grtrack),
           from = start, to = end, chromosome = chr)

EnrichedHeatmap (R)

library(EnrichedHeatmap)
library(rtracklayer)

# Load signal and regions
signal <- import('sample.bw')
tss <- promoters(txdb, upstream = 0, downstream = 1)

# Normalize to matrix
mat <- normalizeToMatrix(signal, tss, extend = 3000, mean_mode = 'w0', w = 50)

# Heatmap
EnrichedHeatmap(mat, name = 'Signal', col = c('white', 'red'))

IGV Batch Screenshot

# Create IGV batch script
cat > igv_batch.txt << 'EOF'
new
genome hg38
load sample.bw
load peaks.bed
goto chr1:1000000-1100000
snapshot region1.png
goto chr2:50000000-51000000
snapshot region2.png
exit
EOF

# Run IGV in batch mode
igv.sh -b igv_batch.txt

Key Tools Comparison

deepTools Key Commands

Related Skills

• peak-calling - Generate peaks for visualization
• peak-annotation - Annotation pie charts
• alignment-files - Prepare BAM files