gabrielmoreira 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>thenhelp(module.function) - R:
packageVersion('<pkg>')then?function_name - CLI:
<tool> --versionthen<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):
- Compute spike-in scale factor externally:
scale = 1 / (spike_reads_per_million)OR per Orlando method - Pass via
--scaleFactor <value>with--normalizeUsing None - Do NOT combine
--scaleFactorwith--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
colormap = RdYlBu_r
[genes]
file = gencode.v44.gtf
title = Genes
height = 5
fontsize = 8
style = UCSC # or 'flybase'; UCSC merges transcripts, flybase shows all
prefered_name = gene_name
merge_transcripts = true
color = '#3C5488'
border_color = 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 = '#D55E00', fill.histogram = '#D55E00')
aTrack <- AnnotationTrack(range = 'peaks.bed', name = 'Peaks',
chromosome = 'chr1', fill = '#888888',
stacking = 'dense')
# Render
plotTracks(list(itrack, axTrack, dTrack, aTrack, grTrack),
from = 1000000, to = 2000000,
sizes = c(1, 1, 3, 1, 4),
background.title = 'transparent',
cex.title = 0.7,
cex.axis = 0.6)
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