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

bio-data-visualization-genome-tracks

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