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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

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

Tool	Type	Best For
deepTools	CLI	Large-scale heatmaps, profiles
ChIPseeker	R	Peak-centric visualization
Gviz	R	Publication-quality browser
EnrichedHeatmap	R	Customizable heatmaps
IGV	GUI	Interactive exploration

deepTools Key Commands

Command	Purpose
bamCoverage	BAM to bigWig
bamCompare	Compare two BAMs
computeMatrix	Signal matrix
plotHeatmap	Heatmap visualization
plotProfile	Profile plot
multiBigwigSummary	Compare multiple bigWigs
plotCorrelation	Sample correlation

Related Skills

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