bio-data-visualization-genome-tracks
Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.
npx skills add BioTender-max/awesome-bio-agent-skills --skill genome-tracks --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: pyGenomeTracks 3.9+, Gviz 1.46+ (Bioconductor), deepTools 3.5+, GenomicRanges 1.54+, IGV 2.18+ (batch mode). Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` - R: `packageVersion('<pkg>')` then `?function_name` - CLI: `<tool> --version` then `<tool> --help` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Genome Browser Tracks **"Plot a genomic locus with multiple tracks"** -> Build a stacked figure where each track (coverage from BigWig, peaks from BED, genes from GTF, Hi-C from cool, loops from BedPE) is aligned to genome coordinates. The decisions that matter: track normalization (especially for ChIP-Rx spike-in), gene-model rendering style (UCSC vs FlyBase), y-axis sharing across samples, and which tool fits the workflow — pyGenomeTracks (config-driven, reproducible, headless), Gviz (R Bioconductor), IGV batch (interactive-tool screenshots). - Python / CLI: `pyGenomeTracks` (Lopez-Delisle 2021 *Bioinformatics* 37:422) - R
- Version Compatibility
- pyGenomeTracks — Config-Driven, Reproducible
- Gviz (R Bioconductor)
- IGV Batch Scripting
- BigWig Generation — The Spike-In Trap
- Track Comparison Across Samples
- Per-Method Failure Modes
- bamCoverage --normalizeUsing undoes spike-in
- Different y-axis across samples
- Wrong gene-model style
- pyGenomeTracks --width interpreted as inches
- Track order top-down vs bottom-up confusion
- Hi-C matrix track depth too low
- IGV batch script silent failures
pyGenomeTracks --tracks tracks.ini \ For multiple regions from a BED: pyGenomeTracks --tracks tracks.ini --BED regions.bed \ batch.txt new genome hg38 load sample.bam load peaks.bed snapshotDirectory ./screenshots goto chr1:1000000-2000000
What does the bio-data-visualization-genome-tracks skill do?
Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill genome-tracks --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.