Bio Phylo Tree Manipulation

Version Compatibility

Reference examples tested with: BioPython 1.83+

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 Manipulation

"Root and prune my phylogenetic tree" → Modify tree topology by rooting with outgroups or midpoint, pruning taxa, collapsing low-support clades, ladderizing branches, or extracting subtrees.

• Python: tree.root_with_outgroup(), tree.prune(), tree.ladderize() (Bio.Phylo)

Modify phylogenetic tree structure: rooting, pruning, ladderizing, and subtree extraction.

Rooting Method Decision

Best practice: Use outgroup rooting with multiple sister-group taxa as the primary method. Validate with MAD. If results conflict, investigate rate variation. A single outgroup taxon gives only ~50% accuracy for root placement, so always prefer multiple outgroups that form a monophyletic group.

Common mistake: Using a very distant outgroup (e.g., a bacterial sequence to root a eukaryotic tree) introduces a long branch that attracts other long branches, distorting topology near the root.

Required Import

from Bio import Phylo
from io import StringIO

Rooting Trees

Root with Outgroup (Preferred)

Goal: Root the tree using known sister-group taxa as outgroup.

Approach: Verify outgroup taxa are monophyletic, then reroot. Prefer multiple outgroup taxa over a single taxon to improve root placement accuracy.

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

# Root with single taxon (acceptable but less reliable)
tree.root_with_outgroup({'name': 'Outgroup'})

# Root with multiple taxa (preferred -- must be monophyletic)
outgroup = [{'name': 'TaxonA'}, {'name': 'TaxonB'}]
if tree.is_monophyletic(outgroup):
    tree.root_with_outgroup(*outgroup)
else:
    print('Outgroup is not monophyletic -- check taxon selection')

Root at Midpoint

Appropriate when no suitable outgroup exists and evolution is approximately clocklike (e.g., viral phylogenetics). Not reliable when substitution rates vary substantially across lineages.

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

MAD Rooting (CLI Alternative)

Minimal Ancestor Deviation rooting is more robust to rate variation than midpoint. Install via pip install mad or use the standalone tool. Useful as a validation of outgroup rooting results.

Check Rooting Status

# 2 children at root = rooted; 3+ = unrooted
root = tree.root
print(f'Root has {len(root.clades)} children')
print(f'Is bifurcating: {tree.is_bifurcating()}')

Ladderizing

Sort clades for consistent visual presentation.

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

# Larger clades at bottom
tree.ladderize()

# Larger clades at top
tree.ladderize(reverse=True)

Phylo.write(tree, 'ladderized.nwk', 'newick')

Pruning Trees

Remove Specific Taxa

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

# Find and remove a taxon
target = tree.find_any(name='TaxonToRemove')
if target:
    tree.prune(target)

# Remove multiple taxa
for name in ['TaxonA', 'TaxonB', 'TaxonC']:
    target = tree.find_any(name=name)
    if target:
        tree.prune(target)

Keep Only Specified Taxa

tree = Phylo.read('tree.nwk', 'newick')
keep_taxa = {'Human', 'Chimp', 'Gorilla'}

terminals = tree.get_terminals()
for term in terminals:
    if term.name not in keep_taxa:
        tree.prune(term)

Collapsing Clades

Collapse branches below a support threshold into polytomies. The threshold depends on the support measure used: >= 70 for standard bootstrap, >= 95 for UFBoot2 (these are not equivalent scales).

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

# Collapse single clade
target = tree.find_any(name='SomeInternalNode')
if target:
    tree.collapse(target)

# Collapse by branch length
tree.collapse_all(lambda c: c.branch_length and c.branch_length < 0.01)

# Collapse poorly-supported nodes
# Use 70 for standard bootstrap; use 95 for UFBoot2 values
tree.collapse_all(lambda c: c.confidence is not None and c.confidence < 70)

Extracting Subtrees

Get Clade as Subtree

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

# Find common ancestor of taxa
clade = tree.common_ancestor({'name': 'Human'}, {'name': 'Chimp'})

# The clade itself can be treated as a subtree
Phylo.draw_ascii(clade)

# Get all terminals in this clade
subtree_taxa = [t.name for t in clade.get_terminals()]
print(f'Subtree contains: {subtree_taxa}')

Extract Subtree by Common Ancestor

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

# Find MRCA (Most Recent Common Ancestor)
taxa = [{'name': 'Human'}, {'name': 'Chimp'}, {'name': 'Gorilla'}]
mrca = tree.common_ancestor(*taxa)
print(f'MRCA branch length: {mrca.branch_length}')

Tree Traversal

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

# Iterate all clades (preorder by default)
for clade in tree.find_clades():
    print(clade.name, clade.branch_length)

# Level-order traversal (breadth-first)
for clade in tree.find_clades(order='level'):
    print(clade.name)

# Postorder traversal
for clade in tree.find_clades(order='postorder'):
    print(clade.name)

# Only terminal nodes
for term in tree.get_terminals():
    print(term.name)

# Only internal nodes
for internal in tree.get_nonterminals():
    print(internal)

Finding Clades

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

# Find by name
clade = tree.find_any(name='Human')

# Find all matching criteria
matches = tree.find_clades(branch_length=lambda x: x and x > 0.5)
for m in matches:
    print(f'{m.name}: {m.branch_length}')

# Find by terminal status
terminals = list(tree.find_clades(terminal=True))
internals = list(tree.find_clades(terminal=False))

Getting Path Between Nodes

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

# Path from root to a node
target = tree.find_any(name='Human')
path = tree.get_path(target)
print(f'Path from root to Human: {len(path)} nodes')
for clade in path:
    print(f'  {clade.name}: {clade.branch_length}')

# Trace path between any two nodes
human = tree.find_any(name='Human')
mouse = tree.find_any(name='Mouse')
trace = tree.trace(human, mouse)
print(f'Path Human to Mouse: {len(trace)} nodes')

Checking Tree Properties

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

# Check if monophyletic
taxa = [tree.find_any(name='Human'), tree.find_any(name='Chimp')]
taxa = [t for t in taxa if t is not None]
print(f'Is monophyletic: {tree.is_monophyletic(taxa)}')

# Check if bifurcating
print(f'Is bifurcating: {tree.is_bifurcating()}')

# Check if preterminal (parent of only terminals)
for clade in tree.get_nonterminals():
    print(f'{clade}: is_preterminal={clade.is_preterminal()}')

Modifying Branch Lengths

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

# Set missing branch lengths
for clade in tree.find_clades():
    if clade.branch_length is None:
        clade.branch_length = 0.0

# Scale all branch lengths
scale_factor = 100  # Convert to percent divergence
for clade in tree.find_clades():
    if clade.branch_length:
        clade.branch_length *= scale_factor

# Remove branch lengths (convert to cladogram)
for clade in tree.find_clades():
    clade.branch_length = None

Renaming Taxa

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

# Rename individual taxon
target = tree.find_any(name='OldName')
if target:
    target.name = 'NewName'

# Batch rename from mapping
name_map = {'Hsap': 'Human', 'Ptro': 'Chimp', 'Mmus': 'Mouse'}
for term in tree.get_terminals():
    if term.name in name_map:
        term.name = name_map[term.name]

Phylo.write(tree, 'renamed.nwk', 'newick')

Counting Nodes

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

n_terminals = len(tree.get_terminals())
n_internals = len(tree.get_nonterminals())
n_total = tree.count_terminals() + len(tree.get_nonterminals())

print(f'Terminals: {n_terminals}')
print(f'Internal nodes: {n_internals}')
print(f'Total nodes: {n_total}')

Tree Depths

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

# Get depths from root
depths = tree.depths()
for clade, depth in depths.items():
    if clade.is_terminal():
        print(f'{clade.name}: depth={depth:.3f}')

# Get maximum depth (tree height)
max_depth = max(depths.values())
print(f'Tree height: {max_depth:.3f}')

Splitting Clades

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

# Split a terminal into multiple children
target = tree.find_any(name='TaxonA')
if target and target.is_terminal():
    target.split(n=2, branch_length=0.05)  # Creates 2 children

# Split with specific branch lengths
target.split(branch_length=[0.1, 0.2, 0.3])  # Creates 3 children

Generating Random Trees

from Bio.Phylo.BaseTree import Tree

# Generate random bifurcating tree
taxa = ['Human', 'Chimp', 'Gorilla', 'Mouse', 'Rat']
random_tree = Tree.randomized(taxa)
Phylo.draw_ascii(random_tree)

# With branch lengths
random_tree = Tree.randomized(taxa, branch_length=1.0)

Quick Reference: Tree Methods

Related Skills

• tree-io - Read and write tree files
• tree-visualization - Draw modified trees
• distance-calculations - Build trees from alignments
• modern-tree-inference - ML tree inference (rooting before/after inference)
• species-trees - Coalescent methods and concordance factors