Genome Tracks

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/data-visualization/genome-tracksThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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,…

---
name: bio-data-visualization-genome-tracks
description: 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.
tool_type: mixed
primary_tool: pyGenomeTracks
---

## 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: `Gviz::plotTracks` (Hahne-Ivanek 2016)
- Interactive: IGV (Robinson 2011 *Nat Biotechnol* 29:24) with batch scripting

## The Single Most Important Modern Insight -- Spike-In Normalization Cannot Be Done With --normalizeUsing

deepTools `bamCoverage` is the canonical BigWig generator. Its `--normalizeUsing` flag accepts {RPKM, CPM, BPM, RPGC, None} — none of which implement ChIP-Rx spike-in normalization. All four divide by *sample-internal* mapped read counts and will UNDO any spike-in correction.

For ChIP-Rx (Orlando 2014 *Cell Rep* 9:1163):
1. Compute spike-in scale factor externally: `scale = 1 / (spike_reads_per_million)` OR per Orlando method
2. Pass via `--scaleFactor <value>` with `--normalizeUsing None`
3. **Do NOT combine `--scaleFactor` with `--normalizeUsing CPM/RPGC`** — re-normalizes the signal and undoes spike-in

This is the most common silent error in ChIP-seq visualization. The BigWig looks fine; the cross-sample comparison is wrong by the spike-in factor.

## pyGenomeTracks — Config-Driven, Reproducible

**Goal:** Render a multi-track locus figure from a config file specifying each track's source file, style, height, and color.

**Approach:** Write an `.ini` file with one section per track; invoke `pyGenomeTracks --tracks tracks.ini --region chr1:1000000-2000000 --outFileName out.pdf`.

```ini
# tracks.ini
[x-axis]
where = top
fontsize = 8

[h3k27ac]
file = h3k27ac.bw
title = H3K27ac
height = 3
color = #D55E00
min_value = 0
max_value = 50
number_of_bins = 700
summary_method = mean
nans_to_zeros = true

[spacer]
height = 0.3

[peaks]
file = h3k27ac_peaks.narrowPeak
title = Peaks
height = 0.8
color = #888888
display = collapsed
labels = false
file_type = narrowPeak

[loops]
file = loops.bedpe
title = Loops
height = 2
file_type = links
links_type = arcs
color = '#0072B2'
line_width = 0.5

[hic]
file = matrix.cool
title = Hi-C (KR-normalized)
height = 8
depth = 1000000
min_value = 0
max_value = auto
transform = log1p
colormap = RdYlBu_r

[genes]
file = gencode.v44.gtf
title = Genes
height = 5
fontsize = 8
style = UCSC                            # or 'flybase'; UCSC merges transcripts, flybase shows all
prefered_name = gene_name
merge_transcripts = true
color = '#3C5488'
border_color = black
```

```bash
pyGenomeTracks --tracks tracks.ini \
    --region chr1:1000000-2000000 \
    --outFileName locus.pdf \
    --width 18 \                          # CENTIMETERS not inches; default 40 cm
    --dpi 300

# For multiple regions from a BED:
pyGenomeTracks --tracks tracks.ini --BED regions.bed \
    --outFileName multi.pdf
```

**`--width` is in centimeters**, not inches. Default 40 cm; Nature double-column = 18.3 cm. `--decreasingXAxis` flips orientation for minus-strand loci.

## Gviz (R Bioconductor)

```r
library(Gviz)
library(GenomicRanges)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)

# Tracks
axTrack <- GenomeAxisTrack()
itrack <- IdeogramTrack(genome = 'hg38', chromosome = 'chr1')

txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
grTrack <- GeneRegionTrack(txdb, genome = 'hg38', chromosome = 'chr1',
                            name = 'Genes', transcriptAnnotation = 'symbol',
                            collapseTranscripts = 'meta')

dTrack <- DataTrack(range = 'h3k27ac.bw', type = 'h',
                     chromosome = 'chr1', name = 'H3K27ac',
                     col.histogram = '#D55E00', fill.histogram = '#D55E00')

aTrack <- AnnotationTrack(range = 'peaks.bed', name = 'Peaks',
                           chromosome = 'chr1', fill = '#888888',
                           stacking = 'dense')

# Render
plotTracks(list(itrack, axTrack, dTrack, aTrack, grTrack),
           from = 1000000, to = 2000000,
           sizes = c(1, 1, 3, 1, 4),
           background.title = 'transparent',
           cex.title = 0.7,
           cex.axis = 0.6)
```

## IGV Batch Scripting

For interactive-tool screenshots without launching the GUI:

```bash
# batch.txt
new
genome hg38
load sample.bam
load peaks.bed
snapshotDirectory ./screenshots
goto chr1:1000000-2000000
sort base
maxPanelHeight 500
snapshot region1.png
goto chr2:5000000-6000000
snapshot region2.png
exit
```

```bash
igv -b batch.txt
```

IGV batch is suita

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install pyGenomeTracks deepTools

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Genome Tracks

• github.com/GPTomics/bioSkills/tree/main/data-visualization/genom — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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