Agent skill

bio-chipseq-visualization

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.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-chipseq-visualization

SKILL.md

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

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