name: bio-chipseq-visualization description: Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract) with SES scaling, k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually. goal_approach_exempt: true origin: openai4s category: bioskills/chip-seq metadata: tool_type: mixed primary_tool: deepTools third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a license: MIT
Reference examples tested with: deepTools 3.5+, pyGenomeTracks 3.9+, Gviz 1.46+, EnrichedHeatmap 1.32+, ChIPseeker 1.38+, IGV 2.17+, samtools 1.19+, bedtools 2.31+.
"Visualize ChIP-seq signal around features of interest" -> Generate normalized signal tracks (bigWig), heatmaps centered on TSS/peaks, average profile plots, and genome-browser views — with normalization that supports the biological claim (within-sample vs cross-sample vs spike-in scaled).
bamCoverage -> computeMatrix -> plotHeatmap / plotProfileThe single most consequential choice is bigWig normalization — it determines whether visual comparison reflects biology. Get this right before generating any heatmap or browser view.
| Goal | Method | When to use |
|---|---|---|
| Within-sample profile of a single ChIP | --normalizeUsing CPM |
Standard; reads per million; comparable within one library |
| Within-sample, length-aware | --normalizeUsing BPM |
TPM-analog; useful for variable-width regions; less common for ChIP-seq |
| Cross-sample with equal effective depth | --normalizeUsing RPGC --effectiveGenomeSize <N> |
"1x genome coverage" — assumes equal sequencing genome-wide; ENCODE convention |
| Cross-condition with global signal change | --scaleFactor <spike_in_derived> (skip --normalizeUsing) |
HDACi / BETi / EZH2i; see chip-seq/spike-in-normalization |
| ChIP vs input ratio | bamCompare --operation log2 |
Visualize enrichment over input |
| ChIP vs input control-subtracted | bamCompare --operation subtract |
Absolute signal above background |
| ChIP vs input SES-corrected | bamCompare --scaleFactorsMethod SES --operation log2 |
More robust to library size; uses signal-extraction-scaling |
ENCODE convention: RPGC with read-length-matched effective genome size. For visual comparison of treatment vs control on a fold-change biology, log2 bamCompare against shared input.
Spike-in scaled tracks (the right way):
# Compute scale factor from spike-in reads (ChIP-Rx Drosophila or CUT&RUN E. coli)
SCALE=$(echo "scale=6; 1.0 / $SPIKE_IN_READS_M" | bc) # 1 per million spike reads
bamCoverage -b chip.bam -o chip.bw --scaleFactor $SCALE --binSize 10
# DO NOT also pass --normalizeUsing; deepTools multiplies the two factors, reintroducing depth normalization
# Standard within-sample (CPM)
bamCoverage -b chip.bam -o chip.bw \
--normalizeUsing CPM --binSize 10 \
--extendReads 200 --numberOfProcessors 8
# Cross-sample at 1x genome coverage (ENCODE)
bamCoverage -b chip.bam -o chip.bw \
--normalizeUsing RPGC --effectiveGenomeSize 2701495761 \
--binSize 10 --extendReads 200
# ChIP vs Input log2 ratio (visualization of enrichment)
bamCompare -b1 chip.bam -b2 input.bam -o chip_vs_input.bw \
--operation log2 --binSize 50 --extendReads 200 \
--pseudocount 1 --skipZeroOverZero
# Compute matrix centered on TSS
computeMatrix reference-point \
--referencePoint TSS \
-b 3000 -a 3000 \
-R genes.bed \
-S chip.bw input.bw \
-o matrix.gz \
--outFileSortedRegions sorted_regions.bed \
--numberOfProcessors 8 \
--skipZeros
# Heatmap with k-means clustering (biology emerges from clusters)
plotHeatmap -m matrix.gz \
-o heatmap.pdf \
--kmeans 3 \
--colorMap RdBu_r \
--zMin -3 --zMax 3 \
--refPointLabel TSS \
--heatmapHeight 12 \
--whatToShow 'heatmap and colorbar'
# Profile plot (average signal across regions)
plotProfile -m matrix.gz \
-o profile.pdf \
--perGroup \
--plotTitle 'H3K4me3 around TSS'
computeMatrix scale-regions \
-R genes.bed \
-S chip.bw \
-b 3000 -a 3000 \
-m 5000 \
-o matrix_genebody.gz \
--numberOfProcessors 8
plotProfile -m matrix_genebody.gz -o genebody_profile.pdf --perGroup
multiBamSummary bins -b sample1.bam sample2.bam sample3.bam \
--binSize 10000 -o results.npz \
--numberOfProcessors 8
plotCorrelation -in results.npz \
--corMethod spearman \
--whatToPlot heatmap \
--plotNumbers -o correlation.pdf \
--outFileCorMatrix correlation.tab
# Replicates should correlate > 0.8 (narrow), > 0.6 (broad)
INI-driven, config-as-code; better than Gviz for complex layouts or pipeline integration.
# tracks.ini
[x-axis]
[chip-h3k27ac]
file = h3k27ac.bw
color = darkblue
height = 3
title = H3K27ac
[chip-h3k4me3]
file = h3k4me3.bw
color = darkred
height = 3
title = H3K4me3
[peaks-narrowpeak]
file = peaks.narrowPeak
file_type = narrow_peak
color = black
height = 0.5
title = MACS peaks
[se-bed]
file = super_enhancers.bed
color = orange
height = 0.5
title = Super-enhancers
[genes]
file = genes.gtf
color = darkgreen
prefered_name = gene_name
height = 4
pyGenomeTracks --tracks tracks.ini --region chr1:1000000-1500000 -o region.pdf
For pipeline-driven figure generation across multiple regions, pyGenomeTracks is easier to script than Gviz. For one-off publication figures with complex annotation, Gviz remains useful.
library(Gviz)
library(GenomicRanges)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
chr <- 'chr1'; start <- 1e6; end <- 1.1e6
itrack <- IdeogramTrack(genome = 'hg38', chromosome = chr)
gtrack <- GenomeAxisTrack()
dtrack <- DataTrack(range = 'sample.bw', genome = 'hg38',
type = 'histogram', name = 'ChIP', col.histogram = 'darkblue')
grtrack <- GeneRegionTrack(TxDb.Hsapiens.UCSC.hg38.knownGene,
genome = 'hg38', chromosome = chr, name = 'Genes')
plotTracks(list(itrack, gtrack, dtrack, grtrack), from = start, to = end, chromosome = chr)
library(EnrichedHeatmap)
library(rtracklayer)
# Normalize bigWig signal to a matrix around target sites
signal <- import('sample.bw')
tss <- promoters(txdb, upstream = 0, downstream = 1)
mat <- normalizeToMatrix(signal, tss, extend = 3000, mean_mode = 'w0', w = 50)
# Heatmap with customization
EnrichedHeatmap(mat, name = 'Signal', col = c('white', 'red'),
top_annotation = HeatmapAnnotation(lines = anno_enriched()))
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
peaks <- readPeakFile('peaks.narrowPeak')
promoter <- getPromoters(TxDb = TxDb.Hsapiens.UCSC.hg38.knownGene,
upstream = 3000, downstream = 3000)
tagMatrix <- getTagMatrix(peaks, windows = promoter)
# Tag heatmap and average profile
# tagHeatmap in ChIPseeker >= 1.36 takes palette (RColorBrewer name), not xlim/color;
# xlim is read from the tagMatrix window. plotAvgProf still uses xlim/conf.
tagHeatmap(tagMatrix, palette = 'Reds')
plotAvgProf(tagMatrix, xlim = c(-3000, 3000), conf = 0.95,
xlab = 'Distance from TSS (bp)', ylab = 'Peak density')
# IGV batch script for reproducible screenshots
cat > igv.batch << 'EOF'
new
genome hg38
load chip.bw
load peaks.bed
load super_enhancers.bed
goto chr1:1000000-1100000
snapshot region1.png
goto chr2:50000000-51000000
snapshot region2.png
exit
EOF
igv.sh -b igv.batch
--normalizeUsing and --scaleFactor conflictTrigger: Passing both --normalizeUsing CPM and --scaleFactor X.
Mechanism: deepTools multiplies the --scaleFactor value by the factor computed from --normalizeUsing, so passing both compounds them and reintroduces library-depth normalization on top of the spike-in factor.
Symptom: Spike-in scaling appears to have no effect; tracks look like CPM.
Fix: Use ONE — --scaleFactor alone for spike-in; --normalizeUsing alone otherwise. Never both.
Trigger: bamCompare --operation log2 without pseudocount; many bins have zero reads.
Mechanism: log2(0/x) = -Inf; downstream tools (plotHeatmap) may color these as NaN or fail.
Fix: Add --pseudocount 1 to both samples; or use --skipZeroOverZero to skip bins with zero in both samples.
Trigger: Using stranded bigWigs (separate plus/minus) with reference-point mode on a BED without strand info.
Mechanism: computeMatrix doesn't auto-detect strand; signal is plotted in genomic-strand orientation, breaking TSS-centered plots.
Fix: Use unstranded merged bigWig OR ensure BED has strand column 6.
--kmeans -- Order depends on first sample onlyTrigger: Using k-means with multiple samples and expecting consistent clustering.
Mechanism: k-means clusters by signal in the first -S bigWig only; other samples are plotted in the same row order.
Fix: Order samples in -S so the most-discriminating one is first; for combined clustering across samples, use --hclust or run k-means externally on combined matrix.
Trigger: Computing scale_factor = spike_reads / 1e6 and passing to --scaleFactor.
Mechanism: deepTools multiplies signal by scaleFactor; the INVERSE is correct (sample with fewer spike reads gets larger scale factor to compensate).
Symptom: Treatment samples appear lower than control even when biology says higher.
Fix: scale_factor = MIN(spike_reads_all_samples) / spike_reads_this_sample. Always verify against known internal-control regions (blacklist should show no signal change post-scaling).
Trigger: Loading a 3 GB bigWig into R as a GRanges.
Mechanism: Gviz loads the entire bigWig into memory for genome-wide views.
Fix: Use chromosome parameter to restrict; use import.bw(con, which = GRanges(...)) to subset; consider pyGenomeTracks for whole-chromosome views.
Trigger: Custom INI keys not recognized; or section names with spaces.
Mechanism: pyGenomeTracks expects exact key names; case-sensitive section labels.
Fix: Run make_tracks_file --trackFiles sample.bw -o tracks.ini to generate a template; modify from there.
| Pattern | Likely cause | Action |
|---|---|---|
| Heatmap shows enrichment; profile plot doesn't | Signal concentrated at few regions; profile averages them out | Both correct; heatmap shows distribution, profile shows central tendency |
| Replicate heatmaps differ at peak edges | Different normalization or stranded vs unstranded bigWigs | Verify bigWig parameters identical; use same --normalizeUsing |
| Spike-in scaled tracks show opposite trend from CPM | Global shift; CPM forces median to control levels | Spike-in is correct; CPM is fooled by composition |
| ChIPseeker tag heatmap differs from deepTools heatmap | ChIPseeker uses peak density; deepTools uses signal coverage | Different metrics; pick one per analysis |
| Profile plot loose-replicate band wide | Genuine biological variability OR one replicate failed | Check per-replicate metrics (chipseq-qc); don't average across failing rep |
| Error / symptom | Cause | Solution |
|---|---|---|
| bigWig has all zeros | Wrong chromosome naming (chr vs no chr) | samtools view -H bam | head to check; convert if needed |
| computeMatrix "all regions skipped" | BED chromosome naming mismatches bigWig | Match seqlevels |
| plotHeatmap colors compressed | --zMin/--zMax not set; outliers dominate |
Set --zMin -3 --zMax 3 or use percentile-based |
| IGV batch hangs | exit command missing; IGV waits for input |
Always end batch script with exit |
| pyGenomeTracks region out of range | Region exceeds chromosome length | Verify region from samtools view -H bam |
| Spike-in scaled track has artifact stripes | Scale factor too extreme (>10x) | Verify spike-in reads adequate (>100k); check titration |