pyGenomeViz

Overview

pyGenomeViz is a genome visualization python package for comparative genomics implemented in 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 format file, and can be used to interactively plot genome visualization figure on jupyter notebook, or for integration into a genome analysis pipeline.

For more information, please see full documentation here.

Installation

Python 3.7 or later is required for installation.

Install PyPI package:

pip install pygenomeviz

Install bioconda package:

conda install -c conda-forge -c bioconda pygenomeviz

Examples

Jupyter notebooks containing code examples below is available here.

Basic Example

Single Genome Track Visualization

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}")

fig = gv.plotfig()

Multiple Genome Track & Link Visualization

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), (701, 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")
gv.add_link(("genome 02", 350, 450), ("genome 03", 450, 350), normal_color="skyblue", inverted_color="lime")
gv.add_link(("genome 02", 900, 700), ("genome 03", 700, 500), normal_color="skyblue", inverted_color="lime")
gv.add_link(("genome 03", 900, 701), ("genome 02", 1150, 950), normal_color="skyblue", inverted_color="lime")

fig = gv.plotfig()

Practical Example

Single Genome Track Visualization from Genbank file

from pygenomeviz import Genbank, GenomeViz, load_dataset

# Load single genbank file
gbk_files, _ = load_dataset("escherichia_phage")
gbk = Genbank(gbk_files[0])

# Visualize genbank features
gv = GenomeViz()
track = gv.add_feature_track(gbk.name, gbk.genome_length)
track.add_genbank_features(gbk)

fig = gv.plotfig()

Multiple Genome Track & Link Visualization from Genbank files

from pygenomeviz import Genbank, GenomeViz, load_dataset

gv = GenomeViz(
    feature_track_ratio=0.5,
    link_track_ratio=1.0,
    tick_track_ratio=0.5,
    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.genome_length)
    track.add_genbank_features(gbk)

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, interpolation_value=link.identity, curve=True)

fig = gv.plotfig()

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.

• Add GC content & GC skew subtrack
• Add annotation (Fill Box, ROI)
• Add colorbar (Experimetal implementation)