bio-data-visualization-genome-tracks
SkillDev toolsLets your agent draw genome track visualizations from biological data.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the bio-data-visualization-genome-tracks skill
About this capability
The largest open-source medical AI skills library for OpenClaw🦞.
What this skill tells your AI
The instructions your AI receives, as published by freedomintelligence/openclaw-medical-skills in skills/bio-data-visualization-genome-tracks/SKILL.md and read by ahel’s review.
name: bio-data-visualization-genome-tracks description: Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks. tool_type: mixed primary_tool: pyGenomeTracks measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Genome Track Visualization
pyGenomeTracks (Python/CLI)
# Create tracks configuration file
cat > tracks.ini << 'EOF'
[bigwig]
file = coverage.bw
title = Coverage
height = 4
color = #4DBBD5
min_value = 0
[spacer]
height = 0.5
[peaks]
file = peaks.bed
title = Peaks
color = #E64B35
height = 2
labels = false
[spacer]
height = 0.5
[genes]
file = genes.gtf
title = Genes
height = 5
fontsize = 10
style = flybase
color = #3C5488
[x-axis]
EOF
# Generate plot
pyGenomeTracks --tracks tracks.ini --region chr1:1000000-2000000 \
--outFileName tracks.png --dpi 150
pyGenomeTracks with Multiple Samples
[sample1 coverage]
file = sample1.bw
title = Sample 1
height = 3
color = #4DBBD5
min_value = 0
max_value = auto
[sample2 coverage]
file = sample2.bw
title = Sample 2
height = 3
color = #E64B35
min_value = 0
max_value = auto
[sample1 peaks]
file = sample1_peaks.narrowPeak
title = Sample 1 Peaks
color = #4DBBD5
height = 1
file_type = narrowPeak
[sample2 peaks]
file = sample2_peaks.narrowPeak
title = Sample 2 Peaks
color = #E64B35
height = 1
file_type = narrowPeak
pyGenomeTracks Programmatic
import pygenometracks.tracks as pygtk
tracks = pygtk.PlotTracks('tracks.ini', fig_width=40, dpi=150)
tracks.plot('output.png', 'chr1', 1000000, 2000000)
Gviz (R)
library(Gviz)
library(GenomicRanges)
# Genome axis
gtrack <- GenomeAxisTrack()
# Ideogram
itrack <- IdeogramTrack(genome = 'hg38', chromosome = 'chr1')
# Gene model
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
grtrack <- GeneRegionTrack(txdb, genome = 'hg38', chromosome = 'chr1',
name = 'Genes', transcriptAnnotation = 'symbol')
# Data track from BigWig
dtrack <- DataTrack(range = 'coverage.bw', genome = 'hg38', chromosome = 'chr1',
name = 'Coverage', type = 'histogram', col.histogram = '#4DBBD5')
# Annotation track from BED
atrack <- AnnotationTrack(range = 'peaks.bed', genome = 'hg38', chromosome = 'chr1',
name = 'Peaks', fill = '#E64B35')
# Plot tracks
plotTracks(list(itrack, gtrack, dtrack, atrack, grtrack),
from = 1000000, to = 2000000, sizes = c(1, 1, 3, 1, 3))
Gviz with Multiple Samples
# Create overlay data track
dtrack1 <- DataTrack(range = 'sample1.bw', name = 'Sample1', col = '#4DBBD5')
dtrack2 <- DataTrack(range = 'sample2.bw', name = 'Sample2', col = '#E64B35')
overlay <- OverlayTrack(trackList = list(dtrack1, dtrack2))
plotTracks(list(gtrack, overlay, grtrack), from = 1000000, to = 2000000,
type = 'histogram', legend = TRUE)
Gviz Customization
# Highlight regions
ht <- HighlightTrack(trackList = list(dtrack, atrack),
start = c(1200000, 1500000),
end = c(1300000, 1600000),
chromosome = 'chr1')
# Custom display parameters
displayPars(dtrack) <- list(
background.title = '#3C5488',
fontcolor.title = 'white',
col.axis = 'black',
ylim = c(0, 100)
)
plotTracks(list(gtrack, ht, grtrack), from = 1000000, to = 2000000)
IGV.js (Web)
<!DOCTYPE html>
<html>
<head>
<script src="https://cdn.jsdelivr.net/npm/igv@3.0.0/dist/igv.min.js"></script>
</head>
<body>
<div id="igv-div"></div>
<script>
var options = {
genome: "hg38",
locus: "chr1:1,000,000-2,000,000",
tracks: [
{
name: "Coverage",
url: "coverage.bw",
type: "wig",
color: "#4DBBD5"
},
{
name: "Peaks",
url: "peaks.bed",
type: "annotation",
color: "#E64B35"
}
]
};
igv.createBrowser(document.getElementById('igv-div'), options);
</script>
</body>
</html>
Create BigWig from BAM
# Using deepTools
bamCoverage -b sample.bam -o coverage.bw \
--binSize 10 --normalizeUsing RPKM --effectiveGenomeSize 2913022398
# Using bedtools + wigToBigWig
bedtools genomecov -bg -ibam sample.bam > coverage.bedGraph
sort -k1,1 -k2,2n coverage.bedGraph > coverage.sorted.bedGraph
bedGraphToBigWig coverage.sorted.bedGraph chrom.sizes coverage.bw
Multi-Region Plot
# pyGenomeTracks with BED regions
pyGenomeTracks --tracks tracks.ini --BED regions.bed \
--outFileName multi_region.pdf --dpi 150
# Gviz with multiple regions
regions <- GRanges(seqnames = 'chr1', ranges = IRanges(start = c(1e6, 2e6), end = c(1.5e6, 2.5e6)))
pdf('multi_region.pdf', width = 10, height = 8)
for (i in seq_along(regions)) {
plotTracks(track_list, from = start(regions[i]), to = end(regions[i]))
}
dev.off()
Related Skills
- genome-intervals/bigwig-tracks - BigWig file handling
- chip-seq/chipseq-visualization - ChIP-specific tracks
- hi-c-analysis/hic-visualization - Hi-C contact maps
Signals
- GitHub stars
- 3k
- Forks
- 407
- Last commit
- Jul 2026
Advanced
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bio-data-visualization-genome-tracks- Source
- github.com/freedomintelligence/openclaw-medical-skills