Quantitative track

TL;DR: BigWig and BedGraph files store genome-wide quantitative signals (read depth, ChIP-seq enrichment, conservation scores). JBrowse renders them as an XY plot, a density heatmap, a line, or a scatter plot, switchable from the track menu's Plot type submenu.

Rendering types

The track menu's Plot type submenu (backed by the display's defaultRendering slot) offers these styles:

XY plot - filled bar chart; good for coverage and discrete peaks
Density - a single-row heatmap where color intensity encodes the value; compact for browsing many tracks at once
Line (step) - traces the tops of the bars as a stepped line; best for dense binned data where each pixel is a real bin
Line (interpolated) - joins the midpoint of each data point straight to the next; smoother for sparse or discrete signals where the stepped plateaus look wrong
Scatter - draws individual points without filling; useful for sparse data and seeing single values

The same BigWig rendered in every plot type at once (XY plot, Density, Line (step), Line (interpolated), and Scatter), so the styles can be compared directly. Switch a track between them from its Plot type menu. Open this view in JBrowse ↗

Score options

Plot type and Resolution are top-level track menu items; the rest of the settings below are grouped under Score.

Autoscale type

Controls the Y-axis range (the display's autoscale slot). All three rescale to the region in view, and differ in how they treat outliers:

Local - the plain min and max of the visible data, so one anomalous position flattens everything else against the axis
Local (99th percentile) - clips the outermost 1% of each sign, which keeps a few extreme positions from setting the scale for the whole track
Local ± 3σ - scales to three standard deviations of the local signal, a harder clip than the percentile when the spikes are very tall

Summary score mode

Zoomed out, a BigWig serves precomputed summary bins rather than per-base values, and this picks which statistic of the bin a pixel draws: Minimum, Maximum, Average, or Whiskers, which draws all three at once as a darker average band inside the lighter min-to-max range (summaryScoreMode). Density mode maps score to color rather than height, so it has no whiskers presentation and draws the average instead.

A narrow peak that is obvious at full resolution can fade out across a whole chromosome, because averaging it over a wide bin flattens it. Maximum keeps it visible.

Other score options

Scale type - switch the Y axis between linear and log scaling; log is useful when signal spans several orders of magnitude
Resolution - JBrowse auto-selects resolution from the view width; use this to override it
Set min/max score - pin the Y axis to specific values for side-by-side comparison across samples

Viewing whole-genome coverage for CNV profiling

To get a chromosome-scale view of copy-number changes:

Open your BigWig track
Show all regions in the assembly to get the whole-genome overview
Set Autoscale type to Local ± 3σ to clip outlier spikes
Increase the Resolution a few times until the profile looks smooth

Drag the bottom edge of the track down to make it taller.

Whole-genome CNV coverage profile from a BigWig file. Each chromosome is shown as a separate region; the signal represents read depth normalized by the pipeline. Copy-number gains appear as elevated signal; losses as depressed signal. Open this view in JBrowse ↗

Do it yourselfIn DesktopSession specIn a notebookWith an agent

The steps behind the figure above, run against your own data.

Open your genome: on the JBrowse Desktop start screen click Open new genome (or Show all available genomes to pick a hosted one).This figure uses hg19, loaded from a bgzip-compressed FASTA (.fa.gz + .fai + .gzi).
Add your own track: File → Open track..., then paste a URL or choose a local file. This one needs a BigWig file (.bw).This figure uses “COLO829 MinION tumor (coverage)”.
Track menu → Score → Autoscale type → Local ± 3σ
Track menu → Settings → numStdDevHow many standard deviations the "Local ± σ" autoscale spans. Nothing sets it from a menu — it only reads back out, as the σ in that option's own label — so it is set on the config.
Track menu → Plot type → Scatter
Track menu → ResolutionStep with Coarser and Finer, 2× per click; the caption between them reads the current bin size. Higher fetches finer bins, and this figure uses 5×.
Track menu → Score → Summary score mode → Average
Type your region of interest into the location box and press Enter.This figure is at chr10.

Open this view in JBrowse Desktop ↗

Opens JBrowse Desktop (JBrowse Desktop 5.0+) at this view and saves it as a reopenable session — swap in your own files afterwards.

Nothing happens? Paste this link into Desktop's Open JBrowse Web link... (start screen, or File → Session):

https://jbrowse.org/code/jb2/main/?config=test_data%2Fconfig_demo.json&session=spec-%7B%22views%22%3A%5B%7B%22type%22%3A%22LinearGenomeView%22%2C%22assembly%22%3A%22hg19%22%2C%22loc%22%3A%22chr10%22%2C%22tracks%22%3A%5B%7B%22trackId%22%3A%22colo_tumor%22%2C%22type%22%3A%22LinearWiggleDisplay%22%2C%22autoscale%22%3A%22localsd%22%2C%22numStdDev%22%3A3%2C%22defaultRendering%22%3A%22scatter%22%2C%22resolution%22%3A5%2C%22summaryScoreMode%22%3A%22avg%22%7D%5D%7D%5D%7D&sessionName=JBrowse+docs%3A+bigwig%2Fwhole_genome_coverage

This session spec pastes after &session=spec- on any JBrowse Web instance.

{
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "hg19",
      "loc": "chr10",
      "tracks": [
        {
          "trackId": "colo_tumor",
          "type": "LinearWiggleDisplay",
          "autoscale": "localsd",
          "numStdDev": 3,
          "defaultRendering": "scatter",
          "resolution": 5,
          "summaryScoreMode": "avg"
        }
      ]
    }
  ]
}

Loaded against config: test_data/config_demo.json

The same view with jbrowse-anywidget. Point the adapter at your own file.

from jbrowse_anywidget import LinearGenomeView, fetch_hub

view = LinearGenomeView(
    assembly=fetch_hub("hg19"),
    location="chr10",
)

view.add_track({
    "type": "QuantitativeTrack",
    "trackId": "my_track",
    "name": "My track",
    "assemblyNames": ["hg19"],
    "adapter": {"type": "BigWigAdapter", "uri": "https://your-server/your-file"},
})

view  # display the widget

Hand this to a coding agent. It rebuilds the figure above headlessly, and the session file is then the thing to edit — swap an adapter uri for your own file and rerun.

cat > session.json <<'JSON'
{
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "hg19",
      "loc": "chr10",
      "tracks": [
        {
          "trackId": "colo_tumor",
          "type": "LinearWiggleDisplay",
          "autoscale": "localsd",
          "numStdDev": 3,
          "defaultRendering": "scatter",
          "resolution": 5,
          "summaryScoreMode": "avg"
        }
      ]
    }
  ]
}
JSON

npx @jbrowse/capture --instance https://jbrowse.org/code/jb2/main/ \
  --config https://jbrowse.org/code/jb2/main/test_data/config_demo.json \
  --session session.json -o figure.png

Using JBrowse with AI agents covers the rest: hosted genomes to point it at, and why waiting for the render is the part that goes wrong.

For tumor vs normal comparisons using two BigWig tracks on the same Y-axis, see Multi-quantitative tracks. To scale this up to a whole cohort, one row per tumor, see the TCGA cohort copy number tutorial.

Coverage is shaped by GC content, mappability, repeats, PCR bias, and (when mapping a divergent strain) hyper-divergent regions, so not every dip or spike is a true copy-number change.

Quantitative track

Rendering types#

Score options#

Autoscale type#

Summary score mode#

Other score options#

Viewing whole-genome coverage for CNV profiling#

See also#