bio-data-visualization-genome-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.
origin	openai4s
category	bioskills/data-visualization
metadata	{"tool_type":"mixed","primary_tool":"pyGenomeTracks","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

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.

# 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
 = RdYlBu_r


 = gencode.v44.gtf
 = Genes
 = 
 = 
 = UCSC                            
 = gene_name
 = 
 = 
 = black

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)

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 =  fill.histogram  

aTrack  AnnotationTrack   name  
                           chromosome   fill  
                           stacking  


plotTracksitrack axTrack dTrack aTrack grTrack
           from   to  
           sizes      
           background.title  
           cex.title  
           cex.axis  

IGV Batch Scripting

For interactive-tool screenshots without launching the GUI:

# 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

igv -b batch.txt

IGV batch is suitable when the workflow requires IGV's specific rendering style (allele frequencies, split-read pairs, soft-clipped sequences) — features pyGenomeTracks and Gviz don't replicate.

BigWig Generation — The Spike-In Trap

# WITHOUT spike-in (e.g., RNA-seq, ATAC-seq):
bamCoverage -b sample.bam -o sample.bw \
    --binSize 10 \
    --normalizeUsing BPM \
    --effectiveGenomeSize 2913022398        # hg38 effective; check for build

# CORRECT ChIP-Rx spike-in:
# 1. Compute scale factor externally
SPIKE_RPM=$(samtools view -c sample.spike.bam) 
SCALE_FACTOR=$(echo "scale=10; 1000000 / $SPIKE_RPM" | bc)

# 2. Apply --scaleFactor with --normalizeUsing None
bamCoverage -b sample.bam -o sample.bw \
    --binSize 10 \
    --normalizeUsing None \                  # CRITICAL: None
    --scaleFactor $SCALE_FACTOR

# INCORRECT (silent error):
bamCoverage -b sample.bam -o sample.bw \
    --normalizeUsing CPM \                   # WRONG: undoes spike-in
    --scaleFactor $SCALE_FACTOR

Track Comparison Across Samples

For multi-sample tracks (control vs treatment), set shared y-axis explicitly:

[sample1_bw]
file = sample1.bw
title = Control
height = 3
color = '#0072B2'
min_value = 0
max_value = 100                              # SHARED max across samples

[sample2_bw]
file = sample2.bw
title = Treatment
height = 3
color = '#D55E00'
min_value = 0
max_value = 100                              # SAME max for visual comparability
overlay_previous = share-y                   # for overlay; omit for stack

Without shared y-axis, the "taller" sample is the one with stronger absolute signal — but the figure visually conflates signal magnitude with rendering scale.

Per-Method Failure Modes

bamCoverage --normalizeUsing undoes spike-in

Trigger: ChIP-Rx workflow using --normalizeUsing CPM AND --scaleFactor.

Mechanism: CPM normalization divides by sample-internal reads; cancels the spike-in factor.

Symptom: Spike-in-normalized tracks look the same as un-normalized; cross-condition comparison wrong.

Fix: --normalizeUsing None with --scaleFactor. Validate by examining tracks at known reference loci where signal should match between samples.

Different y-axis across samples

Trigger: Auto-scaled max_value = auto per-sample.

Mechanism: Each track scales independently to its own max.

Symptom: Visual "looks same" across samples that actually differ in magnitude.

Fix: Set explicit min_value and max_value to the same value across samples.

Wrong gene-model style

Trigger: style = flybase for human data (or vice versa).

Mechanism: UCSC merges overlapping transcripts; flybase shows all isoforms; pile-up of isoforms unreadable for transcript-dense human loci.

Symptom: Gene track is a forest of overlapping arrows.

Fix: style = UCSC for human/mouse; merge_transcripts = true to collapse to canonical isoform.

pyGenomeTracks --width interpreted as inches

Trigger: --width 7 thinking inches.

Mechanism: Default unit is centimeters; --width 7 is 7 cm = 2.75 inches.

Symptom: Tiny figure that doesn't match journal column width.

Fix: --width 18.3 for Nature double column (18.3 cm = 183 mm). --width 8.9 for single column.

Track order top-down vs bottom-up confusion

Trigger: Expecting tracks in config-file order; pyGenomeTracks renders top-to-bottom (config[0] = top).

Mechanism: Convention differs across tools (Gviz top-to-bottom; some browsers bottom-to-top).

Symptom: Gene model at top instead of bottom.

Fix: Verify against config file order; for "genes at bottom" put [genes] section last.

Hi-C matrix track depth too low

Trigger: depth = 100000 for a 2 Mb region.

Mechanism: Hi-C matrix track shows interactions up to depth distance; smaller than region collapses the triangle.

Symptom: Hi-C track shows only a thin band.

Fix: depth should be ≥ half the region width; for 2 Mb region, depth = 1000000 minimum.

IGV batch script silent failures

Trigger: Typo in batch command; IGV continues to next command.

Mechanism: IGV batch mode doesn't fail-fast.

Symptom: Subset of snapshots missing; no error.

Fix: Verify each snapshot was produced; small batches and set echo TRUE for debugging.

Reconciliation: When Tracks Disagree

Pattern	Cause	Action
Tracks look identical pre/post spike-in	--normalizeUsing canceled spike-in	Switch to None + --scaleFactor
Coverage differs between bamCoverage and IGV	Different binning; smoothing default	Specify --binSize explicitly; verify with raw BAM
Peaks in different positions across tools	Different peak-caller output (MACS narrowPeak vs broadPeak)	Document caller; cross-reference upstream chip-seq/peak-calling
Hi-C matrix orientation flipped	Pre-rotation vs post-rotation convention	Most tools assume upper-triangle; check vendor

Quantitative Thresholds

Threshold	Value	Source
pyGenomeTracks --width default	40 cm	Tool default; Nature ~18.3 cm
pyGenomeTracks --dpi recommended	300 for publication	Standard
bamCoverage --binSize typical	10-50 bp	Resolution vs file size trade-off
Hi-C track depth	>= half region width	Tool convention
Effective genome size hg38	2913022398	UCSC

Common Errors

Error / symptom	Cause	Solution
Spike-in normalized tracks look unnormalized	--normalizeUsing canceled spike-in	--normalizeUsing None + --scaleFactor
Y-axis differs across samples	Auto-scaling per-track	Explicit min/max in config
Gene track unreadable	flybase style on dense human locus	UCSC + merge_transcripts = true
Figure tiny	--width interpreted as inches	--width in CM
Hi-C band thin	depth too small	depth >= 0.5 × region width
IGV screenshots missing	Batch error silent	Verify per-snapshot; small batches
Coverage off by 2x	Strand-specific issue	Use --filterRNAstrand or split strands

References

• Hahne F, Ivanek R. 2016. Visualizing genomic data using Gviz and Bioconductor. Methods Mol Biol 1418:335-351.
• Lopez-Delisle L, Rabbani L, Wolff J, et al. 2021. pyGenomeTracks: reproducible plots for multivariate genomic datasets. Bioinformatics 37(3):422-423.
• Orlando DA, Chen MW, Brown VE, et al. 2014. Quantitative ChIP-seq normalization reveals global modulation of the epigenome. Cell Rep 9(3):1163-1170.
• Ramírez F, Ryan DP, Grüning B, et al. 2016. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44(W1):W160-W165.
• Robinson JT, Thorvaldsdóttir H, Winckler W, et al. 2011. Integrative Genomics Viewer. Nat Biotechnol 29(1):24-26.

Related Skills

• alignment-files/bam-statistics - BAM-level QC before bigwig
• chip-seq/peak-calling - Peak files for tracks
• chip-seq/chipseq-visualization - ChIP-seq-specific tracks
• hi-c-analysis/hic-visualization - Hi-C-specific contact maps
• alternative-splicing/sashimi-plots - Splice-junction tracks
• data-visualization/multipanel-figures - Combining track figures
• genome-intervals/bigwig-tracks - BigWig file handling