Version Compatibility

Reference examples tested with: BioPython 1.83+, matplotlib 3.8+

Before using code patterns, verify installed versions match. If versions differ:

• Python:

  pip show <package>

  then

  help(module.function)

  to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Tree Visualization

"Create a publication-quality tree figure" → Draw and customize phylogenetic tree visualizations with colored branches, tip labels, and bootstrap support values using matplotlib.

• Python:

  Bio.Phylo.draw()

  with matplotlib customization

Draw phylogenetic trees using matplotlib integration. Bio.Phylo provides basic rectangular tree plots suitable for quick visualization. For publication-quality figures with complex annotations, circular layouts, or metadata heatmaps, consider the alternatives below.

Visualization Tool Decision

For publication: ggtree (R) or ETE4 (Python) for reproducible, customizable figures. iTOL for rapid prototyping, then export SVG and refine in Illustrator/Inkscape.

For quick exploration: Bio.Phylo (below) or FigTree.

Key ggtree features:

%<+%

geom_cladelabel()

gheatmap()

Tanglegrams (comparing two trees): R:

phytools::cophylo()

Required Import

from Bio import Phylo
import matplotlib.pyplot as plt

ASCII Tree Display

tree = Phylo.read('tree.nwk', 'newick')

# Quick text representation
print(tree)

# ASCII art diagram
Phylo.draw_ascii(tree)

Basic Tree Drawing

tree = Phylo.read('tree.nwk', 'newick')

# Simple plot (opens interactive window)
Phylo.draw(tree)
plt.show()

# Save to file
fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax)
plt.savefig('tree.png', dpi=300, bbox_inches='tight')
plt.close()

Customizing Tree Appearance

fig, ax = plt.subplots(figsize=(12, 10))
Phylo.draw(tree, axes=ax, do_show=False,
           branch_labels=lambda c: f'{c.branch_length:.2f}' if c.branch_length else '',
           label_func=lambda c: c.name if c.is_terminal() else '')

ax.set_title('Phylogenetic Tree')
plt.savefig('custom_tree.png', dpi=300, bbox_inches='tight')
plt.close()

Label Customization

# Custom label function
def custom_labels(clade):
    if clade.is_terminal():
        return clade.name
    elif clade.confidence:
        return f'{clade.confidence:.0f}'
    return ''

fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax, label_func=custom_labels)
plt.savefig('labeled_tree.png', dpi=300)
plt.close()

Branch Labels (Bootstrap, Lengths)

# Show branch lengths
def branch_length_labels(clade):
    if clade.branch_length:
        return f'{clade.branch_length:.3f}'
    return ''

fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax, branch_labels=branch_length_labels)
plt.savefig('with_lengths.png', dpi=300)
plt.close()

# Show bootstrap values (stored in clade.confidence or clade.name for internal nodes)
def bootstrap_labels(clade):
    if not clade.is_terminal() and clade.confidence:
        return f'{clade.confidence:.0f}'
    return ''

Phylo.draw(tree, axes=ax, branch_labels=bootstrap_labels)

Coloring Trees

# Color specific clades before drawing
tree = Phylo.read('tree.nwk', 'newick')

# Set colors for specific clades (PhyloXML trees support this natively)
for clade in tree.find_clades():
    if clade.name and 'Human' in clade.name:
        clade.color = 'red'
    elif clade.name and 'Mouse' in clade.name:
        clade.color = 'blue'

fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax)
plt.savefig('colored_tree.png', dpi=300)
plt.close()

Highlighting Clades

from Bio.Phylo.PhyloXML import BranchColor

# Convert to PhyloXML for color support
phyloxml_tree = tree.as_phyloxml()

# Color a clade and its descendants
target = phyloxml_tree.find_any(name='Human')
if target:
    target.color = BranchColor.from_name('red')

fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(phyloxml_tree, axes=ax)
plt.savefig('highlighted.png', dpi=300)
plt.close()

Multiple Output Formats

tree = Phylo.read('tree.nwk', 'newick')
tree.ladderize()

fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax, do_show=False)

# PNG (raster, good for presentations)
plt.savefig('tree.png', dpi=300, bbox_inches='tight')

# PDF (vector, good for publications)
plt.savefig('tree.pdf', bbox_inches='tight')

# SVG (vector, good for web)
plt.savefig('tree.svg', bbox_inches='tight')

plt.close()

Figure Size and Layout

# Adjust figure size based on tree size
n_taxa = len(tree.get_terminals())
height = max(8, n_taxa * 0.3)  # Scale with number of taxa

fig, ax = plt.subplots(figsize=(10, height))
Phylo.draw(tree, axes=ax, do_show=False)
plt.tight_layout()
plt.savefig('scaled_tree.png', dpi=300)
plt.close()

Phylo.draw() Parameters

tree

axes

label_func

branch_labels

do_show

Pre-Processing for Better Visualization

tree = Phylo.read('tree.nwk', 'newick')

# Ladderize for cleaner appearance
tree.ladderize(reverse=True)

# Set missing branch lengths to small value
for clade in tree.find_clades():
    if clade.branch_length is None:
        clade.branch_length = 0.001

fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax)
plt.savefig('clean_tree.png', dpi=300)
plt.close()

Side-by-Side Tree Comparison

tree1 = Phylo.read('tree1.nwk', 'newick')
tree2 = Phylo.read('tree2.nwk', 'newick')

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))

Phylo.draw(tree1, axes=ax1, do_show=False)
ax1.set_title('Tree 1')

Phylo.draw(tree2, axes=ax2, do_show=False)
ax2.set_title('Tree 2')

plt.tight_layout()
plt.savefig('comparison.png', dpi=300)
plt.close()

Hide Axis and Frame

fig, ax = plt.subplots(figsize=(10, 8))
Phylo.draw(tree, axes=ax, do_show=False)

ax.axis('off')  # Remove axis
ax.set_frame_on(False)  # Remove frame

plt.savefig('clean_tree.png', dpi=300, bbox_inches='tight', transparent=True)
plt.close()

Deprecated Functions

draw_graphviz()

Phylo.draw()

For radial (circular) tree layouts, use ggtree (R), ETE4, or iTOL. Bio.Phylo only supports rectangular layouts.

Common Issues

do_show=True

do_show=False

Related Skills

• tree-io - Read and write tree files
• tree-manipulation - Ladderize and reroot before visualization
• distance-calculations - Build trees from alignments for visualization
• modern-tree-inference - ML tree inference produces trees for visualization
• data-visualization/ggplot2-fundamentals - R-based visualization for ggtree users