Bio data visualization genome tracks

Skill FridrichMethod/awesome-skills/skills/bio-data-visualization-genome-tracks

Curated, auto-synced collection of 2,000+ Claude Code & Codex skills for AI4Protein, bioinformatics, AI development, and academic paper writing. One curl command installs them all.

Install

npx -y skills add FridrichMethod/awesome-skills --skill bio-data-visualization-genome-tracks

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

5.8 KB, as published. Nobody here has run it

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/[email protected]/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