Agent skill

bio-data-visualization-genome-tracks

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.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/genome-tracks

SKILL.md

Genome Track Visualization

pyGenomeTracks (Python/CLI)

bash
# 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

ini
[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

python
import pygenometracks.tracks as pygtk

tracks = pygtk.PlotTracks('tracks.ini', fig_width=40, dpi=150)
tracks.plot('output.png', 'chr1', 1000000, 2000000)

Gviz (R)

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

r
# 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

r
# 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)

html
<!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

bash
# 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

bash
# pyGenomeTracks with BED regions
pyGenomeTracks --tracks tracks.ini --BED regions.bed \
    --outFileName multi_region.pdf --dpi 150
r
# 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

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