A multi-track genome browser view that displays different types of genomic data aligned along a shared chromosomal coordinate axis. Multiple parallel horizontal tracks (gene annotations, read coverage, variants, regulatory elements) are stacked vertically, each showing a different data type at the same genomic locus. This visualization is essential for integrative genomics, enabling researchers to explore relationships between gene structure, expression, variation, and regulation in a single coordinated view.
""" anyplot.ai
genome-track-multi: Genome Track Viewer
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 89/100 | Updated: 2026-06-02
"""
import os
import sys
import numpy as np
import pandas as pd
# Work around naming conflict with plotnine.py script and plotnine package
_script_dir = os.path.dirname(os.path.abspath(__file__))
for _p in [_script_dir, "", "."]:
if _p in sys.path:
sys.path.remove(_p)
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_hline,
geom_point,
geom_rect,
geom_ribbon,
geom_segment,
geom_text,
ggplot,
guide_legend,
guides,
labs,
scale_color_manual,
scale_fill_manual,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
np.random.seed(42)
# Theme-adaptive chrome — Imprint palette
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint categorical palette — 8 hues, hybrid-v3 sort
IMPRINT = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
# Genomic region: chr7, EGFR locus (~55kb region)
chrom = "chr7"
region_start = 55_086_000
region_end = 55_141_000
# Track vertical layout (bottom to top):
# Regulatory: 0.0 – 1.0
# Variants: 1.5 – 3.5
# Coverage: 4.0 – 6.0
# Genes: 6.5 – 7.5
# Shaded track backgrounds for regulatory and coverage tracks (theme-adaptive)
track_bg_shaded = pd.DataFrame(
{"xmin": [region_start, region_start], "xmax": [region_end, region_end], "ymin": [0.0, 4.0], "ymax": [1.0, 6.0]}
)
# Track labels (bold, theme-adaptive ink)
track_labels = pd.DataFrame(
{
"x": [region_start + 800] * 4,
"y": [0.88, 3.35, 5.88, 7.38],
"label": ["Regulatory", "Variants", "Coverage", "Genes"],
}
)
# --- Gene Track (center at y=7.0) ---
gene_center = 7.0
exon_half = 0.25
exons = pd.DataFrame(
{
"start": [
55_086_725,
55_087_058,
55_088_850,
55_092_340,
55_095_262,
55_097_600,
55_099_310,
55_110_700,
55_117_550,
55_124_950,
55_131_800,
55_136_500,
55_139_800,
],
"end": [
55_087_020,
55_087_350,
55_089_150,
55_092_580,
55_095_530,
55_097_870,
55_099_550,
55_110_960,
55_117_810,
55_125_200,
55_132_050,
55_136_750,
55_140_100,
],
}
)
exons["ymin"] = gene_center - exon_half
exons["ymax"] = gene_center + exon_half
intron_lines = pd.DataFrame(
{
"x": exons["end"].iloc[:-1].values,
"xend": exons["start"].iloc[1:].values,
"y": [gene_center] * (len(exons) - 1),
"yend": [gene_center] * (len(exons) - 1),
}
)
# Strand direction chevrons (+ strand) — more prominent than previous iteration
chevron_x = np.linspace(region_start + 3000, region_end - 3000, 25)
chevron_size = 400
chevrons_up = pd.DataFrame(
{
"x": chevron_x,
"xend": chevron_x + chevron_size,
"y": [gene_center] * len(chevron_x),
"yend": [gene_center + 0.14] * len(chevron_x),
}
)
chevrons_down = pd.DataFrame(
{
"x": chevron_x,
"xend": chevron_x + chevron_size,
"y": [gene_center] * len(chevron_x),
"yend": [gene_center - 0.14] * len(chevron_x),
}
)
gene_label = pd.DataFrame({"x": [(region_start + region_end) / 2], "y": [gene_center + 0.42], "label": ["EGFR (+)"]})
# --- Coverage Track (y: 4.0 – 6.0) ---
cov_base = 4.0
cov_height = 2.0
positions = np.arange(region_start, region_end, 150)
raw_coverage = 25 + 12 * np.sin(np.linspace(0, 3.5 * np.pi, len(positions)))
for _, exon in exons.iterrows():
mask = (positions >= exon["start"] - 800) & (positions <= exon["end"] + 800)
raw_coverage[mask] += np.random.uniform(15, 45)
raw_coverage += np.random.normal(0, 4, len(positions))
raw_coverage = np.maximum(raw_coverage, 0)
max_cov = raw_coverage.max()
normalized_cov = raw_coverage / max_cov * cov_height
coverage_df = pd.DataFrame({"x": positions, "ymin": [cov_base] * len(positions), "ymax": cov_base + normalized_cov})
# --- Variant Track (y: 1.5 – 3.5) ---
var_base = 1.5
var_height = 2.0
n_variants = 18
variant_positions = np.sort(np.random.randint(region_start + 500, region_end - 500, n_variants))
variant_quality = np.random.uniform(10, 100, n_variants)
variant_types = np.random.choice(["SNP", "Indel"], n_variants, p=[0.75, 0.25])
normalized_quality = variant_quality / 100.0 * var_height
variant_stems = pd.DataFrame(
{
"x": variant_positions,
"xend": variant_positions,
"y": [var_base] * n_variants,
"yend": var_base + normalized_quality,
}
)
variant_heads = pd.DataFrame(
{
"x": variant_positions,
"y": var_base + normalized_quality,
"variant_type": pd.Categorical(variant_types, categories=["SNP", "Indel"], ordered=True),
}
)
# Imprint positions 1→2: SNP (brand green, first series), Indel (lavender — no proximity with ochre Enhancers)
variant_palette = {"SNP": IMPRINT[0], "Indel": IMPRINT[1]}
# --- Regulatory Track (y: 0.0 – 1.0, center at 0.5) ---
reg_center = 0.5
reg_half = 0.25
regulatory_elements = pd.DataFrame(
{
"start": [55_086_200, 55_088_400, 55_093_100, 55_098_900, 55_112_300, 55_120_000, 55_128_500, 55_135_200],
"end": [55_086_700, 55_088_800, 55_093_500, 55_099_250, 55_112_700, 55_120_450, 55_128_900, 55_135_600],
"feature_type": pd.Categorical(
["Promoter", "Enhancer", "Enhancer", "Promoter", "Enhancer", "Insulator", "Enhancer", "Promoter"],
categories=["Promoter", "Enhancer", "Insulator"],
ordered=True,
),
}
)
regulatory_elements["ymin"] = reg_center - reg_half
regulatory_elements["ymax"] = reg_center + reg_half
# Imprint positions 3→5: Promoter (blue), Enhancer (ochre), Insulator (red)
regulatory_palette = {"Promoter": IMPRINT[2], "Enhancer": IMPRINT[3], "Insulator": IMPRINT[4]}
# Track separator lines (theme-adaptive dashed)
separators = pd.DataFrame({"yintercept": [1.25, 3.75, 6.25]})
# Build plot with grammar-of-graphics layer composition
plot = (
ggplot()
# Shaded track backgrounds (regulatory and coverage tracks)
+ geom_rect(
data=track_bg_shaded,
mapping=aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"),
fill=ELEVATED_BG,
alpha=0.8,
)
# Track separators (theme-adaptive)
+ geom_hline(data=separators, mapping=aes(yintercept="yintercept"), color=INK_MUTED, size=0.4, linetype="dashed")
# Gene track: intron connector lines
+ geom_segment(data=intron_lines, mapping=aes(x="x", xend="xend", y="y", yend="yend"), color=INK_SOFT, size=0.6)
# Gene track: strand chevrons — increased size and alpha vs previous iteration
+ geom_segment(
data=chevrons_up, mapping=aes(x="x", xend="xend", y="y", yend="yend"), color=INK_SOFT, size=0.55, alpha=0.85
)
+ geom_segment(
data=chevrons_down, mapping=aes(x="x", xend="xend", y="y", yend="yend"), color=INK_SOFT, size=0.55, alpha=0.85
)
# Gene track: exon rectangles (Imprint blue)
+ geom_rect(
data=exons,
mapping=aes(xmin="start", xmax="end", ymin="ymin", ymax="ymax"),
fill=IMPRINT[2],
color=PAGE_BG,
size=0.25,
)
# Gene track: EGFR gene label
+ geom_text(
data=gene_label,
mapping=aes(x="x", y="y", label="label"),
size=3.5,
color=INK,
fontstyle="italic",
fontweight="bold",
)
# Coverage track: filled area (Imprint cyan)
+ geom_ribbon(
data=coverage_df,
mapping=aes(x="x", ymin="ymin", ymax="ymax"),
fill=IMPRINT[5],
alpha=0.65,
color=IMPRINT[5],
size=0.2,
)
# Variant track: stems (muted structural lines)
+ geom_segment(data=variant_stems, mapping=aes(x="x", xend="xend", y="y", yend="yend"), color=INK_MUTED, size=0.5)
# Variant track: lollipop heads with Imprint colors (generates native legend)
+ geom_point(data=variant_heads, mapping=aes(x="x", y="y", color="variant_type"), size=3, alpha=0.9)
+ scale_color_manual(name="Variant Type", values=variant_palette, guide=guide_legend(order=1))
# Regulatory track: colored rectangles with Imprint palette (generates native legend)
+ geom_rect(
data=regulatory_elements, mapping=aes(xmin="start", xmax="end", ymin="ymin", ymax="ymax", fill="feature_type")
)
+ scale_fill_manual(name="Regulatory", values=regulatory_palette, guide=guide_legend(order=2))
# Track labels (theme-adaptive, bold)
+ geom_text(
data=track_labels,
mapping=aes(x="x", y="y", label="label"),
size=3.5,
ha="left",
fontweight="bold",
color=INK_SOFT,
)
# Axes
+ scale_x_continuous(
labels=lambda x: [f"{int(v):,}" for v in x], limits=(region_start, region_end), expand=(0.01, 0)
)
+ scale_y_continuous(limits=(-0.2, 7.8), breaks=[], expand=(0, 0))
+ labs(title="genome-track-multi · python · plotnine · anyplot.ai", x=f"Genomic Position ({chrom})", y="")
# Theme-adaptive chrome (Imprint palette canonical tokens)
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
plot_title=element_text(size=12, weight="bold", color=INK),
axis_title_x=element_text(size=10, color=INK),
axis_title_y=element_blank(),
axis_text_x=element_text(size=8, color=INK_SOFT),
axis_text_y=element_blank(),
axis_ticks_major_y=element_blank(),
legend_title=element_text(size=8, weight="bold", color=INK),
legend_text=element_text(size=8, color=INK_SOFT),
legend_position="right",
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_key_size=10,
panel_grid_minor=element_blank(),
panel_grid_major_x=element_line(color=INK, size=0.3, alpha=0.15),
panel_grid_major_y=element_blank(),
panel_background=element_rect(fill=PAGE_BG, color=None),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
)
+ guides(fill=guide_legend(override_aes={"alpha": 1}))
)
# Save — landscape 3200×1800 px (8 in × 4.5 in @ 400 dpi)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in")
Part of Genome Track Viewer on anyplot.ai.