Genome browser

An IGV-like view of every condition at one locus — signal tracks (with replicate averaging and shared y-axes), peak calls, and gene models.

Table of contents

1. What the browser is for
2. Tracks are a dict, types are auto-detected
3. Replicate averaging, the browser way
4. Shared y-axes for honest comparison
5. Reading the result

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What the browser is for

Heatmaps and enrichment summarise thousands of regions; a browser view zooms into one locus to see the data — the per-base coverage, where peaks were called, and which genes are nearby. It is how you sanity-check a result and how you build a figure for a specific gene.

Tracks are a dict, types are auto-detected

browser(region, tracks) takes a region and an ordered dict of named tracks. Each value’s type is inferred from its extension or Python type:

track value	rendered as
*.bw path (or a list of them)	binned coverage (a list is averaged)
*.bed / *.narrowPeak path, or a Loci	interval rectangles
a Genes object	stacked gene models (exons / CDS)
*.bedpe path / list[Pair]	arc track

from genomeblocks import browser

region = "chr19:50,792,009-50,923,669"
tracks = {
    "ATAC 0h":   [BW["ATAC_0h_r1"], BW["ATAC_0h_r2"]],   # list -> averaged
    "ATAC 4h":   [BW["ATAC_4h_r1"], BW["ATAC_4h_r2"]],
    "AR 0h":     BW["AR_0h"],
    "AR 4h":     BW["AR_4h"],
    "FOXA1 0h":  BW["FOXA1_0h"],
    "FOXA1 4h":  BW["FOXA1_4h"],
    "AR 4h peaks":    PEAK["AR_4h"],
    "FOXA1 4h peaks": PEAK["FOXA1_4h"],
    "genes":     genes,
}

The region accepts a "chr:start-end" string (commas allowed), a (chrom, start, end) tuple, or a Locus.

Replicate averaging, the browser way

The two ATAC replicates are passed as a list of bigWig paths — the browser extracts each and averages their per-bin means into a single track. This is the same operation the heatmap does over its track columns, so the two figures show the ATAC replicates consistently.

Shared y-axes for honest comparison

By default each bigWig track auto-scales to its own maximum — which makes a low-signal 0 h track look as tall as a high-signal 4 h track and hides the induction. bw_share groups tracks that should share one y-scale (the group’s region maximum):

fig, axes = browser(region, tracks, bw_n_bins=2000, figsize=(11, None),
                    bw_share=[["ATAC 0h", "ATAC 4h"],
                              ["AR 0h", "AR 4h"],
                              ["FOXA1 0h", "FOXA1 4h"]])

Now AR 0 h and AR 4 h sit on the same axis, so the DHT-induced AR gain — and the ATAC and FOXA1 changes — are read directly off the heights.

control	effect
bw_n_bins	bins per bigWig track (horizontal resolution)
bw_share	list of name-groups that share a y-scale
bw_ymax	a fixed y-max: a scalar for all bigWig tracks, or a per-track dict
figsize=(w, None)	width fixed; height derived from the track heights

browser returns (fig, axes_by_name), so you can grab any track’s axis by name to annotate it (highlight a peak, mark a TSS) before saving.

Reading the result

At this prostate locus (the KLK locus on chr19) you can see the logic of the whole analysis in one panel: ATAC marks the accessible landscape, FOXA1 occupies sites at 0 h and 4 h, and AR appears/strengthens at 4 h — strongest where FOXA1 is already bound (the AR+F sites) — with the called peaks and gene models lined up underneath.

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That completes the walkthrough. The full, runnable notebook is at examples/ar_foxa1_lncap/; for per-module reference see the User Guide.