Version Compatibility

Reference examples tested with: GenomicRanges 1.54+, deepTools 3.5+

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

ChIP-seq Visualization

"Create a heatmap of ChIP-seq signal around peaks" → Generate signal heatmaps, profile plots, and genome browser tracks showing enrichment patterns around genomic features.

• CLI: deeptools computeMatrix reference-point → plotHeatmap
• R: Gviz, ChIPseeker::plotAvgProf()

deepTools - Compute Matrix

Goal: Build a signal matrix of ChIP-seq coverage around reference points for downstream heatmaps and profiles.

Approach: Use computeMatrix to extract bigWig signal values in windows around genomic features like TSS.

bash
# 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

Goal: Visualize ChIP signal across gene bodies scaled to a uniform length.

Approach: Scale all gene regions to equal size and compute signal with flanking windows.

bash
# 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

Goal: Generate a heatmap of ChIP-seq signal intensity across genomic regions.

Approach: Render the precomputed signal matrix as a clustered heatmap with optional profile summary.

bash
# 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

Goal: Display average ChIP-seq signal profiles across genomic regions for sample comparison.

Approach: Plot mean signal from the computed matrix, optionally overlaying multiple samples.

bash
# 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

Goal: Convert BAM alignments to normalized bigWig signal tracks for visualization.

Approach: Use bamCoverage for single-sample normalization or bamCompare for log2 ratio of ChIP over input.

bash
# 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)

Goal: Visualize peak distribution around TSS using ChIPseeker tag matrices and profile plots.

Approach: Build a tag density matrix from peak locations relative to promoter windows, then plot as heatmap or average profile.

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)

Goal: Create publication-quality genome browser views combining signal tracks, gene models, and ideograms.

Approach: Layer Gviz track objects (ideogram, axis, data, gene) and render a specific genomic region.

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

Goal: Compare ChIP-seq signal from multiple samples in a single browser view.

Approach: Create separate DataTrack objects per sample and stack them in the plotTracks call.

r
# 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)

Goal: Generate customizable heatmaps of ChIP signal around genomic features using ComplexHeatmap framework.

Approach: Normalize bigWig signal to a matrix around target sites and render with 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

Goal: Automate genome browser screenshots at specific loci without manual interaction.

Approach: Write an IGV batch script that loads tracks, navigates to regions, and saves snapshots.

bash
# 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