bio-chipseq-visualization
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, SES), 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-
npx skills add BioTender-max/awesome-bio-agent-skills --skill chipseq-visualization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility 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+. # ChIP-seq Visualization **"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). - CLI (production): deepTools `bamCoverage` -> `computeMatrix` -> `plotHeatmap` / `plotProfile` - CLI (config-driven tracks): pyGenomeTracks (replaces Gviz for many use cases) - R (publication): Gviz, EnrichedHeatmap, ChIPseeker tag heatmaps - GUI: IGV with batch scripts for reproducible screenshots The single most consequential choice is **bigWig normalization** — it determines whether visual comparison reflects biology. Get this right before generating any heatmap or browser view. ## bigWig Normalization Decision Tree | Goal | Method | When to use | |------|--------|------------| | Within-sample profile of a single ChIP | `--normalizeUsing CPM` | Standard; reads per million; comp
- Version Compatibility
- bigWig Normalization Decision Tree
- deepTools Workflow
- bigWig generation
- Signal matrix and heatmap (reference-point: TSS / peak summit)
- Scale-regions (gene-body scaled to common length)
- Sample correlation
- pyGenomeTracks (Modern Browser-Style Plotting)
- R: Gviz and EnrichedHeatmap
- ChIPseeker Tag Heatmap (R)
- IGV Batch Scripts
- Per-Tool Failure Modes
- bamCoverage -- --normalizeUsing and --scaleFactor conflict
- bamCompare -- log2 with zeros produces -Inf
Compute scale factor from spike-in reads (ChIP-Rx Drosophila or CUT&RUN E. coli) bamCoverage -b chip.bam -o chip.bw --scaleFactor $SCALE --binSize 10 DO NOT also pass --normalizeUsing; mutually exclusive Standard within-sample (CPM) bamCoverage -b chip.bam -o chip.bw \ Cross-sample at 1x genome coverage (ENCODE) ChIP vs Input log2 ratio (visualization of enrichment) bamCompare -b1 chip.bam -b2 input.bam -o chip_vs_input.bw \ Compute matrix centered on TSS computeMatrix reference-point \
What does the bio-chipseq-visualization skill do?
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, SES), 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-
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill chipseq-visualization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.