Worked Examples¶
These examples use public Blueprint hub tracks and focus on realistic PlotNado workflows built from real files.
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from pathlib import Path
import plotnado
from plotnado import GenomicFigure, PlotStyle
rna_gene_of_interest = "ACTB"
monocyte_gene_of_interest = "LYZ"
repo_root = Path(plotnado.__file__).resolve().parent.parent
blueprint_bigwig_files = {
"T12-15 plasma cell RNA": "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/tonsil/T12-15/plasma_cell/RNA-Seq/IDIBAPS/T12-15-PC.signal.star_grape2_crg.GRCh38.20150815.bw",
"T12-16 plasma cell RNA": "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/tonsil/T12-16/plasma_cell/RNA-Seq/IDIBAPS/T12-16-PC.signal.star_grape2_crg.GRCh38.20150815.bw",
}
blueprint_monocyte_h3k27ac_signal = "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/venous_blood/C000S5/CD14-positive_CD16-negative_classical_monocyte/ChIP-Seq/NCMLS/C000S5H2.ERX173536.H3K27ac.bwa.GRCh38.20150529.bw"
blueprint_monocyte_h3k27ac_peaks = "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/venous_blood/C000S5/CD14-positive_CD16-negative_classical_monocyte/ChIP-Seq/NCMLS/C000S5H2.ERX173536.H3K27ac.bwa.GRCh38.20150527.bb"
blueprint_monocyte_candidate_bed = repo_root / "examples" / "data" / "blueprint_monocyte_lyz_candidate_peaks.bed"
from pathlib import Path
import plotnado
from plotnado import GenomicFigure, PlotStyle
rna_gene_of_interest = "ACTB"
monocyte_gene_of_interest = "LYZ"
repo_root = Path(plotnado.__file__).resolve().parent.parent
blueprint_bigwig_files = {
"T12-15 plasma cell RNA": "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/tonsil/T12-15/plasma_cell/RNA-Seq/IDIBAPS/T12-15-PC.signal.star_grape2_crg.GRCh38.20150815.bw",
"T12-16 plasma cell RNA": "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/tonsil/T12-16/plasma_cell/RNA-Seq/IDIBAPS/T12-16-PC.signal.star_grape2_crg.GRCh38.20150815.bw",
}
blueprint_monocyte_h3k27ac_signal = "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/venous_blood/C000S5/CD14-positive_CD16-negative_classical_monocyte/ChIP-Seq/NCMLS/C000S5H2.ERX173536.H3K27ac.bwa.GRCh38.20150529.bw"
blueprint_monocyte_h3k27ac_peaks = "http://ftp.ebi.ac.uk/pub/databases/blueprint/data/homo_sapiens/GRCh38/venous_blood/C000S5/CD14-positive_CD16-negative_classical_monocyte/ChIP-Seq/NCMLS/C000S5H2.ERX173536.H3K27ac.bwa.GRCh38.20150527.bb"
blueprint_monocyte_candidate_bed = repo_root / "examples" / "data" / "blueprint_monocyte_lyz_candidate_peaks.bed"
Example 1: stacked Blueprint BigWig tracks with label boxes¶
This mirrors the main workflow closely: two real Blueprint RNA BigWig files, separate panels, and the track title and scale drawn inside the plotted area with a visible background box.
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stacked_fig = GenomicFigure()
stacked_fig.scalebar()
stacked_fig.genes("hg38")
stacked_fig.autocolor()
for name, url in blueprint_bigwig_files.items():
stacked_fig.bigwig(
url,
title=name,
style=PlotStyle.FRAGMENT,
height=0.55,
autoscale_group="blueprint_rna",
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.5,
scale_height=0.5,
plot_scale=True,
)
stacked_plot = stacked_fig.plot_gene(rna_gene_of_interest)
stacked_plot
stacked_fig = GenomicFigure()
stacked_fig.scalebar()
stacked_fig.genes("hg38")
stacked_fig.autocolor()
for name, url in blueprint_bigwig_files.items():
stacked_fig.bigwig(
url,
title=name,
style=PlotStyle.FRAGMENT,
height=0.55,
autoscale_group="blueprint_rna",
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.5,
scale_height=0.5,
plot_scale=True,
)
stacked_plot = stacked_fig.plot_gene(rna_gene_of_interest)
stacked_plot
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Example 2: Blueprint bigwig_overlay() with a shared axis¶
bigwig_overlay() is the clearer API for this case. It resolves to the same implementation as overlay(), but the current implementation auto-wraps path inputs as BigWigTrack, so the more specific name matches the actual behavior better. The overlay now accepts the same style kwarg as bigwig(), so this example uses PlotStyle.FRAGMENT to keep the signal geometry consistent with the stacked panels above.
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overlay_fig = GenomicFigure()
overlay_fig.scalebar()
overlay_fig.genes("hg38")
overlay_fig.bigwig_overlay(
list(blueprint_bigwig_files.values()),
title="Blueprint plasma-cell RNA overlay",
style=PlotStyle.FRAGMENT,
colors=["#FF9D1B", "#1E5DF8"],
alpha=0.65,
height=0.9,
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.5,
scale_height=0.5,
plot_scale=True,
)
overlay_plot = overlay_fig.plot_gene(rna_gene_of_interest)
overlay_plot
overlay_fig = GenomicFigure()
overlay_fig.scalebar()
overlay_fig.genes("hg38")
overlay_fig.bigwig_overlay(
list(blueprint_bigwig_files.values()),
title="Blueprint plasma-cell RNA overlay",
style=PlotStyle.FRAGMENT,
colors=["#FF9D1B", "#1E5DF8"],
alpha=0.65,
height=0.9,
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.5,
scale_height=0.5,
plot_scale=True,
)
overlay_plot = overlay_fig.plot_gene(rna_gene_of_interest)
overlay_plot
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Example 3: a real review plot with Blueprint BigWig, Blueprint bigBed, and a checked-in BED¶
This mirrors a more realistic workflow: inspect a public signal track, compare it to the hub's peak calls, and add a checked-in BED file that captures the loci you want to revisit after review.
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review_fig = GenomicFigure()
review_fig.scalebar()
review_fig.genes("hg38")
review_fig.bigwig(
blueprint_monocyte_h3k27ac_signal,
title="Blueprint monocyte H3K27ac",
style=PlotStyle.FRAGMENT,
height=0.6,
color="#d9485f",
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.5,
scale_height=0.5,
plot_scale=True,
)
review_fig.bed(
blueprint_monocyte_h3k27ac_peaks,
title="Blueprint H3K27ac peaks (bigBed)",
color="#f59e0b",
draw_edges=False,
height=0.42,
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.7,
show_labels=False,
)
review_fig.bed(
str(blueprint_monocyte_candidate_bed),
title="Curated follow-up peaks (BED)",
color="#0f766e",
draw_edges=True,
show_labels=True,
label_field="name",
font_size=7,
height=0.5,
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.7,
)
review_plot = review_fig.plot_gene(monocyte_gene_of_interest)
review_plot
review_fig = GenomicFigure()
review_fig.scalebar()
review_fig.genes("hg38")
review_fig.bigwig(
blueprint_monocyte_h3k27ac_signal,
title="Blueprint monocyte H3K27ac",
style=PlotStyle.FRAGMENT,
height=0.6,
color="#d9485f",
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.5,
scale_height=0.5,
plot_scale=True,
)
review_fig.bed(
blueprint_monocyte_h3k27ac_peaks,
title="Blueprint H3K27ac peaks (bigBed)",
color="#f59e0b",
draw_edges=False,
height=0.42,
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.7,
show_labels=False,
)
review_fig.bed(
str(blueprint_monocyte_candidate_bed),
title="Curated follow-up peaks (BED)",
color="#0f766e",
draw_edges=True,
show_labels=True,
label_field="name",
font_size=7,
height=0.5,
label_on_track=True,
label_box_enabled=True,
label_box_alpha=0.95,
title_height=0.7,
)
review_plot = review_fig.plot_gene(monocyte_gene_of_interest)
review_plot
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Example 4: start from a public UCSC hub and keep the notebook interactive¶
GenomicFigure.from_ucsc_hub() can flatten supported tracks from a UCSC-style hub directly into a PlotNado figure. This Blueprint hub expands to a larger review figure than you usually want on first render, so the example below keeps just the H3K27ac peak/signal pair and the stranded RNA tracks visible, then uses track_visibility_widget() to turn the rest back on as needed.
The widget is notebook-only and requires ipywidgets, either via pip install "plotnado[notebook]" or uv pip install ipywidgets.
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from plotnado import GenomicFigure
blueprint_hub_url = "https://ftp.ebi.ac.uk/pub/databases/blueprint/releases/current_release/homo_sapiens/hub/hub.txt"
lyz_region = "chr12:6908053-6997143"
hub_fig = GenomicFigure.from_ucsc_hub(
blueprint_hub_url,
genome="hg38",
include_hidden=False,
)
hub_keep_tokens = (
"H3K27ac MASC2_peak",
"H3K27ac MACS2_wiggler",
"RNA-Seq RNAMinus ",
"RNA-Seq RNAPlus ",
)
for track in getattr(hub_fig, "tracks", []):
title = getattr(track, "title", "") or ""
if track.__class__.__name__ in {"ScaleBar", "GenomicAxis"}:
continue
if not any(token in title for token in hub_keep_tokens):
track.height = 0.0
hub_widget = hub_fig.track_visibility_widget(lyz_region)
hub_widget
from plotnado import GenomicFigure
blueprint_hub_url = "https://ftp.ebi.ac.uk/pub/databases/blueprint/releases/current_release/homo_sapiens/hub/hub.txt"
lyz_region = "chr12:6908053-6997143"
hub_fig = GenomicFigure.from_ucsc_hub(
blueprint_hub_url,
genome="hg38",
include_hidden=False,
)
hub_keep_tokens = (
"H3K27ac MASC2_peak",
"H3K27ac MACS2_wiggler",
"RNA-Seq RNAMinus ",
"RNA-Seq RNAPlus ",
)
for track in getattr(hub_fig, "tracks", []):
title = getattr(track, "title", "") or ""
if track.__class__.__name__ in {"ScaleBar", "GenomicAxis"}:
continue
if not any(token in title for token in hub_keep_tokens):
track.height = 0.0
hub_widget = hub_fig.track_visibility_widget(lyz_region)
hub_widget
VBox(children=(HTML(value='<strong>Track visibility</strong>'), VBox(children=(Checkbox(value=True, descriptio…