pygenomeviz 0.3.2

A genome visualization python package for comparative genomics

pyGenomeViz

Table of contents

• Overview
• Installation
• API Examples
• CLI Examples
• Interactive HTML Viewer
• Inspiration

Overview

pyGenomeViz is a genome visualization python package for comparative genomics implemented based on matplotlib. This package is developed for the purpose of easily and beautifully plotting genomic features and sequence similarity comparison links between multiple genomes. It supports genome visualization of Genbank/GFF format file and can be saved figure in various formats (JPG/PNG/SVG/PDF/HTML). User can use pyGenomeViz for interactive genome visualization figure plotting on jupyter notebook, or automatic genome visualization figure plotting in genome analysis scripts/pipelines.

For more information, please see full documentation here.

Fig.1 pyGenomeViz example plot gallery

Installation

Python 3.8 or later is required for installation.

Install PyPI package:

pip install pygenomeviz

Install bioconda package:

conda install -c conda-forge -c bioconda pygenomeviz

Use Docker (Image Registry):

docker pull ghcr.io/moshi4/pygenomeviz:latest
docker run -it --rm ghcr.io/moshi4/pygenomeviz:latest pgv-pmauve -h

API Examples

Jupyter notebooks containing code examples below is available here.

Basic Example

Single Track

from pygenomeviz import GenomeViz

name, genome_size = "Tutorial 01", 5000
cds_list = ((100, 900, -1), (1100, 1300, 1), (1350, 1500, 1), (1520, 1700, 1), (1900, 2200, -1), (2500, 2700, 1), (2700, 2800, -1), (2850, 3000, -1), (3100, 3500, 1), (3600, 3800, -1), (3900, 4200, -1), (4300, 4700, -1), (4800, 4850, 1))

gv = GenomeViz()
track = gv.add_feature_track(name, genome_size)
for idx, cds in enumerate(cds_list, 1):
    start, end, strand = cds
    track.add_feature(start, end, strand, label=f"CDS{idx:02d}")

gv.savefig("example01.png")

Multiple Tracks & Links

from pygenomeviz import GenomeViz

genome_list = (
    {"name": "genome 01", "size": 1000, "cds_list": ((150, 300, 1), (500, 700, -1), (750, 950, 1))},
    {"name": "genome 02", "size": 1300, "cds_list": ((50, 200, 1), (350, 450, 1), (700, 900, -1), (950, 1150, -1))},
    {"name": "genome 03", "size": 1200, "cds_list": ((150, 300, 1), (350, 450, -1), (500, 700, -1), (700, 900, -1))},
)

gv = GenomeViz(tick_style="axis")
for genome in genome_list:
    name, size, cds_list = genome["name"], genome["size"], genome["cds_list"]
    track = gv.add_feature_track(name, size)
    for idx, cds in enumerate(cds_list, 1):
        start, end, strand = cds
        track.add_feature(start, end, strand, label=f"gene{idx:02d}", linewidth=1, labelrotation=0, labelvpos="top", labelhpos="center", labelha="center")

# Add links between "genome 01" and "genome 02"
gv.add_link(("genome 01", 150, 300), ("genome 02", 50, 200))
gv.add_link(("genome 01", 700, 500), ("genome 02", 900, 700))
gv.add_link(("genome 01", 750, 950), ("genome 02", 1150, 950))
# Add links between "genome 02" and "genome 03"
gv.add_link(("genome 02", 50, 200), ("genome 03", 150, 300), normal_color="skyblue", inverted_color="lime", curve=True)
gv.add_link(("genome 02", 350, 450), ("genome 03", 450, 350), normal_color="skyblue", inverted_color="lime", curve=True)
gv.add_link(("genome 02", 900, 700), ("genome 03", 700, 500), normal_color="skyblue", inverted_color="lime", curve=True)
gv.add_link(("genome 03", 900, 700), ("genome 02", 1150, 950), normal_color="skyblue", inverted_color="lime", curve=True)

gv.savefig("example02.png")

Exon Features

from pygenomeviz import GenomeViz

exon_regions1 = [(0, 210), (300, 480), (590, 800), (850, 1000), (1030, 1300)]
exon_regions2 = [(1500, 1710), (2000, 2480), (2590, 2800)]
exon_regions3 = [(3000, 3300), (3400, 3690), (3800, 4100), (4200, 4620)]

gv = GenomeViz()
track = gv.add_feature_track(name=f"Exon Features", size=5000)
track.add_exon_feature(exon_regions1, strand=1, plotstyle="box", label="box", labelrotation=0, labelha="center")
track.add_exon_feature(exon_regions2, strand=-1, plotstyle="arrow", label="arrow", labelrotation=0, labelha="center", facecolor="darkgrey", intron_patch_kws={"ec": "red"})

exon_labels = [f"exon{i+1}" for i in range(len(exon_regions3))]
track.add_exon_feature(exon_regions3, strand=1, plotstyle="bigarrow", label="bigarrow", facecolor="lime", linewidth=1, exon_labels=exon_labels, labelrotation=0, labelha="center", exon_label_kws={"y": 0, "va": "center", "color": "blue"})

gv.savefig("example03.png")

Practical Example

Add Features from Genbank file

from pygenomeviz import Genbank, GenomeViz, load_dataset

gbk_files, _ = load_dataset("enterobacteria_phage")
gbk = Genbank(gbk_files[0])

gv = GenomeViz()
track = gv.add_feature_track(gbk.name, gbk.range_size)
track.add_genbank_features(gbk)

gv.savefig("example04.png")

Add Features from GFF file

from pygenomeviz import Gff, GenomeViz, load_example_gff

gff_file = load_example_gff("enterobacteria_phage.gff")
gff = Gff(gff_file, min_range=5000, max_range=25000)

gv = GenomeViz(fig_track_height=0.7, tick_track_ratio=0.5, tick_style="bar")
track = gv.add_feature_track(gff.name, size=gff.range_size, start_pos=gff.min_range)
track.add_gff_features(gff, plotstyle="arrow", facecolor="tomato")
track.set_sublabel()

gv.savefig("example05.png")

Multiple Tracks & Links from Genbank files

from pygenomeviz import Genbank, GenomeViz, load_dataset

gv = GenomeViz(
    fig_track_height=0.7,
    feature_track_ratio=0.2,
    tick_track_ratio=0.4,
    tick_style="bar",
    align_type="center",
)

gbk_files, links = load_dataset("escherichia_phage")
for gbk_file in gbk_files:
    gbk = Genbank(gbk_file)
    track = gv.add_feature_track(gbk.name, gbk.range_size)
    track.add_genbank_features(gbk, facecolor="limegreen", linewidth=0.5, arrow_shaft_ratio=1.0)

for link in links:
    link_data1 = (link.ref_name, link.ref_start, link.ref_end)
    link_data2 = (link.query_name, link.query_start, link.query_end)
    gv.add_link(link_data1, link_data2, v=link.identity, curve=True)

gv.savefig("example06.png")

Customization Tips

Since pyGenomeViz is implemented based on matplotlib, users can easily customize the figure in the manner of matplotlib. Here are some tips for figure customization.

Customization Tips 01

• Add GC Content & GC skew subtrack
• Add annotation label & fillbox
• Add colorbar for links identity

Code

from pygenomeviz import Genbank, GenomeViz, load_dataset

gv = GenomeViz(
    fig_width=12,
    fig_track_height=0.7,
    feature_track_ratio=0.5,
    tick_track_ratio=0.3,
    tick_style="axis",
    tick_labelsize=10,
)

gbk_files, links = load_dataset("erwinia_phage")
gbk_list = [Genbank(gbk_file) for gbk_file in gbk_files]
for gbk in gbk_list:
    track = gv.add_feature_track(gbk.name, gbk.range_size, labelsize=15)
    track.add_genbank_features(gbk, plotstyle="arrow")

min_identity = int(min(link.identity for link in links))
for link in links:
    link_data1 = (link.ref_name, link.ref_start, link.ref_end)
    link_data2 = (link.query_name, link.query_start, link.query_end)
    gv.add_link(link_data1, link_data2, v=link.identity, vmin=min_identity)

# Add subtracks to top track for plotting 'GC content' & 'GC skew'
gv.top_track.add_subtrack(ratio=0.7, name="gc_content")
gv.top_track.add_subtrack(ratio=0.7, name="gc_skew")

fig = gv.plotfig()

# Add label annotation to top track
top_track = gv.top_track  # or, gv.get_track("MT939486") or gv.get_tracks()[0]
label, start, end = "Inverted", 310000 + top_track.offset, 358000 + top_track.offset
center = int((start + end) / 2)
top_track.ax.hlines(1.5, start, end, colors="red", linewidth=1, linestyles="dashed", clip_on=False)
top_track.ax.text(center, 2.0, label, fontsize=12, color="red", ha="center", va="bottom")

# Add fillbox to top track
x, y = (start, start, end, end), (1, -1, -1, 1)
top_track.ax.fill(x, y, fc="lime", linewidth=0, alpha=0.1, zorder=-10)

# Plot GC content for top track
pos_list, gc_content_list = gbk_list[0].calc_gc_content()
pos_list += gv.top_track.offset  # Offset is required if align_type is not 'left'
gc_content_ax = gv.top_track.subtracks[0].ax
gc_content_ax.set_ylim(bottom=0, top=max(gc_content_list))
gc_content_ax.fill_between(pos_list, gc_content_list, alpha=0.2, color="blue")
gc_content_ax.text(gv.top_track.offset, max(gc_content_list) / 2, "GC(%) ", ha="right", va="center", color="blue")

# Plot GC skew for top track
pos_list, gc_skew_list = gbk_list[0].calc_gc_skew()
pos_list += gv.top_track.offset  # Offset is required if align_type is not 'left'
gc_skew_abs_max = max(abs(gc_skew_list))
gc_skew_ax = gv.top_track.subtracks[1].ax
gc_skew_ax.set_ylim(bottom=-gc_skew_abs_max, top=gc_skew_abs_max)
gc_skew_ax.fill_between(pos_list, gc_skew_list, alpha=0.2, color="red")
gc_skew_ax.text(gv.top_track.offset, 0, "GC skew ", ha="right", va="center", color="red")

# Set coloarbar for link
gv.set_colorbar(fig, vmin=min_identity)

fig.savefig("example07.png")

Customization Tips 02

• Add legends
• Add colorbar for links identity

Code

from matplotlib.lines import Line2D
from matplotlib.patches import Patch

from pygenomeviz import Genbank, GenomeViz, load_dataset

gv = GenomeViz(
    fig_width=10,
    fig_track_height=0.5,
    feature_track_ratio=0.5,
    tick_track_ratio=0.3,
    align_type="center",
    tick_style="bar",
    tick_labelsize=10,
)

gbk_files, links = load_dataset("enterobacteria_phage")
for idx, gbk_file in enumerate(gbk_files):
    gbk = Genbank(gbk_file)
    track = gv.add_feature_track(gbk.name, gbk.range_size, labelsize=10)
    track.add_genbank_features(
        gbk,
        label_type="product" if idx == 0 else None,  # Labeling only top track
        label_handle_func=lambda s: "" if s.startswith("hypothetical") else s,  # Ignore 'hypothetical ~~~' label
        labelsize=8,
        labelvpos="top",
        facecolor="skyblue",
        linewidth=0.5,
    )

normal_color, inverted_color, alpha = "chocolate", "limegreen", 0.5
min_identity = int(min(link.identity for link in links))
for link in links:
    link_data1 = (link.ref_name, link.ref_start, link.ref_end)
    link_data2 = (link.query_name, link.query_start, link.query_end)
    gv.add_link(link_data1, link_data2, normal_color, inverted_color, alpha, v=link.identity, vmin=min_identity, curve=True)

fig = gv.plotfig()

# Add Legends (Maybe there is a better way)
handles = [
    Line2D([], [], marker=">", color="skyblue", label="CDS", ms=10, ls="none"),
    Patch(color=normal_color, label="Normal Link"),
    Patch(color=inverted_color, label="Inverted Link"),
]
fig.legend(handles=handles, bbox_to_anchor=(1, 1))

# Set colorbar for link
gv.set_colorbar(fig, bar_colors=[normal_color, inverted_color], alpha=alpha, vmin=min_identity, bar_label="Identity", bar_labelsize=10)

fig.savefig("example08.png")

CLI Examples

pyGenomeViz provides CLI workflow for visualization of genome alignment or reciprocal best-hit CDS search results with MUMmer or MMseqs or progressiveMauve. Each CLI workflow requires the installation of additional dependent tools to run.

MUMmer CLI Workflow Example

See pgv-mummer document for details.

Download example dataset: pgv-download-dataset -n erwinia_phage

:warning: MUMmer must be installed in advance to run

pgv-mummer --gbk_resources MT939486.gbk MT939487.gbk MT939488.gbk LT960552.gbk \
           -o mummer_example --tick_style axis --align_type left --feature_plotstyle arrow

MMseqs CLI Workflow Example

See pgv-mmseqs document for details.

Download example dataset: pgv-download-dataset -n enterobacteria_phage

:warning: MMseqs must be installed in advance to run

pgv-mmseqs --gbk_resources NC_019724.gbk NC_024783.gbk NC_016566.gbk NC_013600.gbk NC_031081.gbk NC_028901.gbk \
           -o mmseqs_example --fig_track_height 0.7 --feature_linewidth 0.3 --tick_style bar --curve \
           --normal_link_color chocolate --inverted_link_color limegreen --feature_color skyblue

progressiveMauve CLI Workflow Example

See pgv-pmauve document for details.

Download example dataset: pgv-download-dataset -n escherichia_coli

:warning: progressiveMauve must be installed in advance to run

pgv-pmauve --seq_files NC_000913.gbk NC_002695.gbk NC_011751.gbk NC_011750.gbk \
           -o pmauve_example --tick_style bar

Interactive HTML Viewer

pyGenomeViz implements HTML file output functionality for interactive data visualization. In API, HTML file can be output using savefig_html method. In CLI, user can select HTML file output option. As shown below, data tooltip display, pan/zoom, object color change, text change, etc are available in HTML viewer (Demo Page).

Inspiration

pyGenomeViz was inspired by

• GenomeDiagram (BioPython)
• Easyfig
• genoplotR
• gggenomes