Etetoolkit

Common patterns:

from ete3 import Tree

# Load tree from file
tree = Tree("tree.nw", format=1)

# Basic statistics
print(f"Leaves: {len(tree)}")
print(f"Total nodes: {len(list(tree.traverse()))}")

# Prune to taxa of interest
taxa_to_keep = ["species1", "species2", "species3"]
tree.prune(taxa_to_keep, preserve_branch_length=True)

# Midpoint root
midpoint = tree.get_midpoint_outgroup()
tree.set_outgroup(midpoint)

# Save modified tree
tree.write(outfile="rooted_tree.nw")

Use scripts/tree_operations.py for command-line tree manipulation:

# Display tree statistics
python scripts/tree_operations.py stats tree.nw

# Convert format
python scripts/tree_operations.py convert tree.nw output.nw --in-format 0 --out-format 1

# Reroot tree
python scripts/tree_operations.py reroot tree.nw rooted.nw --midpoint

# Prune to specific taxa
python scripts/tree_operations.py prune tree.nw pruned.nw --keep-taxa "sp1,sp2,sp3"

# Show ASCII visualization
python scripts/tree_operations.py ascii tree.nw

2. Phylogenetic Analysis

Analyze gene trees with evolutionary event detection:

• Sequence alignment integration: Link trees to multiple sequence alignments (FASTA, Phylip)
• Species naming: Automatic or custom species extraction from gene names
• Evolutionary events: Detect duplication and speciation events using Species Overlap or tree reconciliation
• Orthology detection: Identify orthologs and paralogs based on evolutionary events
• Gene family analysis: Split trees by duplications, collapse lineage-specific expansions

Workflow for gene tree analysis:

from ete3 import PhyloTree

# Load gene tree with alignment
tree = PhyloTree("gene_tree.nw", alignment="alignment.fasta")

# Set species naming function
def get_species(gene_name):
    return gene_name.split("_")[0]

tree.set_species_naming_function(get_species)

# Detect evolutionary events
events = tree.get_descendant_evol_events()

# Analyze events
for node in tree.traverse():
    if hasattr(node, "evoltype"):
        if node.evoltype == "D":
            print(f"Duplication at {node.name}")
        elif node.evoltype == "S":
            print(f"Speciation at {node.name}")

# Extract ortholog groups
ortho_groups = tree.get_speciation_trees()
for i, ortho_tree in enumerate(ortho_groups):
    ortho_tree.write(outfile=f"ortholog_group_{i}.nw")

Finding orthologs and paralogs:

# Find orthologs to query gene
query = tree & "species1_gene1"

orthologs = []
paralogs = []

for event in events:
    if query in event.in_seqs:
        if event.etype == "S":
            orthologs.extend([s for s in event.out_seqs if s != query])
        elif event.etype == "D":
            paralogs.extend([s for s in event.out_seqs if s != query])

3. NCBI Taxonomy Integration

Integrate taxonomic information from NCBI Taxonomy database:

• Database access: Automatic download and local caching of NCBI taxonomy (~300MB)
• Taxid/name translation: Convert between taxonomic IDs and scientific names
• Lineage retrieval: Get complete evolutionary lineages
• Taxonomy trees: Build species trees connecting specified taxa
• Tree annotation: Automatically annotate trees with taxonomic information

Building taxonomy-based trees:

from ete3 import NCBITaxa

ncbi = NCBITaxa()

# Build tree from species names
species = ["Homo sapiens", "Pan troglodytes", "Mus musculus"]
name2taxid = ncbi.get_name_translator(species)
taxids = [name2taxid[sp][0] for sp in species]

# Get minimal tree connecting taxa
tree = ncbi.get_topology(taxids)

# Annotate nodes with taxonomy info
for node in tree.traverse():
    if hasattr(node, "sci_name"):
        print(f"{node.sci_name} - Rank: {node.rank} - TaxID: {node.taxid}")

Annotating existing trees:

# Get taxonomy info for tree leaves
for leaf in tree:
    species = extract_species_from_name(leaf.name)
    taxid = ncbi.get_name_translator([species])[species][0]

    # Get lineage
    lineage = ncbi.get_lineage(taxid)
    ranks = ncbi.get_rank(lineage)
    names = ncbi.get_taxid_translator(lineage)

    # Add to node
    leaf.add_feature("taxid", taxid)
    leaf.add_feature("lineage", [names[t] for t in lineage])

4. Tree Visualization

Create publication-quality tree visualizations:

• Output formats: PNG (raster), PDF, and SVG (vector) for publications
• Layout modes: Rectangular and circular tree layouts
• Interactive GUI: Explore trees interactively with zoom, pan, and search
• Custom styling: NodeStyle for node appearance (colors, shapes, sizes)
• Faces: Add graphical elements (text, images, charts, heatmaps) to nodes
• Layout functions: Dynamic styling based on node properties

Basic visualization workflow:

from ete3 import Tree, TreeStyle, NodeStyle

tree = Tree("tree.nw")

# Configure tree style
ts = TreeStyle()
ts.show_leaf_name = True
ts.show_branch_support = True
ts.scale = 50  # pixels per branch length unit

# Style nodes
for node in tree.traverse():
    nstyle = NodeStyle()

    if node.is_leaf():
        nstyle["fgcolor"] = "blue"
        nstyle["size"] = 8
    else:
        # Color by support
        if node.support > 0.9:
            nstyle["fgcolor"] = "darkgreen"
        else:
            nstyle["fgcolor"] = "red"
        nstyle["size"] = 5

    node.set_style(nstyle)

# Render to file
tree.render("tree.pdf", tree_style=ts)
tree.render("tree.png", w=800, h=600, units="px", dpi=300)

Use scripts/quick_visualize.py for rapid visualization:

# Basic visualization
python scripts/quick_visualize.py tree.nw output.pdf

# Circular layout with custom styling
python scripts/quick_visualize.py tree.nw output.pdf --mode c --color-by-support

# High-resolution PNG
python scripts/quick_visualize.py tree.nw output.png --width 1200 --height 800 --units px --dpi 300

# Custom title and styling
python scripts/quick_visualize.py tree.nw output.pdf --title "Species Phylogeny" --show-support

Advanced visualization with faces:

from ete3 import Tree, TreeStyle, TextFace, CircleFace

tree = Tree("tree.nw")

# Add features to nodes
for leaf in tree:
    leaf.add_feature("habitat", "marine" if "fish" in leaf.name else "land")

# Layout function
def layout(node):
    if node.is_leaf():
        # Add colored circle
        color = "blue" if node.habitat == "marine" else "green"
        circle = CircleFace(radius=5, color=color)
        node.add_face(circle, column=0, position="aligned")

        # Add label
        label = TextFace(node.name, fsize=10)
        node.add_face(label, column=1, position="aligned")

ts = TreeStyle()
ts.layout_fn = layout
ts.show_leaf_name = False

tree.render("annotated_tree.pdf", tree_style=ts)

5. Clustering Analysis

Analyze hierarchical clustering results with data integration:

• ClusterTree: Specialized class for clustering dendrograms
• Data matrix linking: Connect tree leaves to numerical profiles
• Cluster metrics: Silhouette coefficient, Dunn index, inter/intra-cluster distances
• Validation: Test cluster quality with different distance metrics
• Heatmap visualization: Display data matrices alongside trees

Clustering workflow:

from ete3 import ClusterTree

# Load tree with data matrix
matrix = """#Names\tSample1\tSample2\tSample3
Gene1\t1.5\t2.3\t0.8
Gene2\t0.9\t1.1\t1.8
Gene3\t2.1\t2.5\t0.5"""

tree = ClusterTree("((Gene1,Gene2),Gene3);", text_array=matrix)

# Evaluate cluster quality
for node in tree.traverse():
    if not node.is_leaf():
        silhouette = node.get_silhouette()
        dunn = node.get_dunn()

        print(f"Cluster: {node.name}")
        print(f"  Silhouette: {silhouette:.3f}")
        print(f"  Dunn index: {dunn:.3f}")

# Visualize with heatmap
tree.show("heatmap")

6. Tree Comparison

Quantify topological differences between trees:

• Robinson-Foulds distance: Standard metric for tree comparison
• Normalized RF: Scale-invariant distance (0.0 to 1.0)
• Partition analysis: Identify unique and shared bipartitions
• Consensus trees: Analyze support across multiple trees
• Batch comparison: Compare multiple trees pairwise

Compare two trees:

from ete3 import Tree

tree1 = Tree("tree1.nw")
tree2 = Tree("tree2.nw")

# Calculate RF distance
rf, max_rf, common_leaves, parts_t1, parts_t2 = tree1.robinson_foulds(tree2)

print(f"RF distance: {rf}/{max_rf}")
print(f"Normalized RF: {rf/max_rf:.3f}")
print(f"Common leaves: {len(common_leaves)}")

# Find unique partitions
unique_t1 = parts_t1 - parts_t2
unique_t2 = parts_t2 - parts_t1

print(f"Unique to tree1: {len(unique_t1)}")
print(f"Unique to tree2: {len(unique_t2)}")

Compare multiple trees:

import numpy as np

trees = [Tree(f"tree{i}.nw") for i in range(4)]

# Create distance matrix
n = len(trees)
dist_matrix = np.zeros((n, n))

for i in range(n):
    for j in range(i+1, n):
        rf, max_rf, _, _, _ = trees[i].robinson_foulds(trees[j])
        norm_rf = rf / max_rf if max_rf > 0 else 0
        dist_matrix[i, j] = norm_rf
        dist_matrix[j, i] = norm_rf

Installation and Setup

Install ETE toolkit:

# Basic installation
uv pip install ete3

# With external dependencies for rendering (optional but recommended)
# On macOS:
brew install qt@5

# On Ubuntu/Debian:
sudo apt-get install python3-pyqt5 python3-pyqt5.qtsvg

# For full features including GUI
uv pip install ete3[gui]

First-time NCBI Taxonomy setup:

The first time NCBITaxa is instantiated, it automatically downloads the NCBI taxonomy database (~300MB) to ~/.etetoolkit/taxa.sqlite. This happens only once:

from ete3 import NCBITaxa
ncbi = NCBITaxa()  # Downloads database on first run

Update taxonomy database:

ncbi.update_taxonomy_database()  # Download latest NCBI data

Common Use Cases

Use Case 1: Phylogenomic Pipeline

Complete workflow from gene tree to ortholog identification:

from ete3 import PhyloTree, NCBITaxa

# 1. Load gene tree with alignment
tree = PhyloTree("gene_tree.nw", alignment="alignment.fasta")

# 2. Configure species naming
tree.set_species_naming_function(lambda x: x.split("_")[0])

# 3. Detect evolutionary events
tree.get_descendant_evol_events()

# 4. Annotate with taxonomy
ncbi = NCBITaxa()
for leaf in tree:
    if leaf.species in species_to_taxid:
        taxid = species_to_taxid[leaf.species]
        lineage = ncbi.get_lineage(taxid)
        leaf.add_feature("lineage", lineage)

# 5. Extract ortholog groups
ortho_groups = tree.get_speciation_trees()

# 6. Save and visualize
for i, ortho in enumerate(ortho_groups):
    ortho.write(outfile=f"ortho_{i}.nw")

Use Case 2: Tree Preprocessing and Formatting

Batch process trees for analysis:

# Convert format
python scripts/tree_operations.py convert input.nw output.nw --in-format 0 --out-format 1

# Root at midpoint
python scripts/tree_operations.py reroot input.nw rooted.nw --midpoint

# Prune to focal taxa
python scripts/tree_operations.py prune rooted.nw pruned.nw --keep-taxa taxa_list.txt

# Get statistics
python scripts/tree_operations.py stats pruned.nw

Use Case 3: Publication-Quality Figures

Create styled visualizations:

from ete3 import Tree, TreeStyle, NodeStyle, TextFace

tree = Tree("tree.nw")

# Define clade colors
clade_colors = {
    "Mammals": "red",
    "Birds": "blue",
    "Fish": "green"
}

def layout(node):
    # Highlight clades
    if node.is_leaf():
        for clade, color in clade_colors.items():
            if clade in node.name:
                nstyle = NodeStyle()
                nstyle["fgcolor"] = color
                nstyle["size"] = 8
                node.set_style(nstyle)
    else:
        # Add support values
        if node.support > 0.95:
            support = TextFace(f"{node.support:.2f}", fsize=8)
            node.add_face(support, column=0, position="branch-top")

ts = TreeStyle()
ts.layout_fn = layout
ts.show_scale = True

# Render for publication
tree.render("figure.pdf", w=200, units="mm", tree_style=ts)
tree.render("figure.svg", tree_style=ts)  # Editable vector

Use Case 4: Automated Tree Analysis

Process multiple trees systematically:

from ete3 import Tree
import os

input_dir = "trees"
output_dir = "processed"

for filename in os.listdir(input_dir):
    if filename.endswith(".nw"):
        tree = Tree(os.path.join(input_dir, filename))

        # Standardize: midpoint root, resolve polytomies
        midpoint = tree.get_midpoint_outgroup()
        tree.set_outgroup(midpoint)
        tree.resolve_polytomy(recursive=True)

        # Filter low support branches
        for node in tree.traverse():
            if hasattr(node, 'support') and node.support < 0.5:
                if not node.is_leaf() and not node.is_root():
                    node.delete()

        # Save processed tree
        output_file = os.path.join(output_dir, f"processed_{filename}")
        tree.write(outfile=output_file)

Reference Documentation

For comprehensive API documentation, code examples, and detailed guides, refer to the following resources in the references/ directory:

• api_reference.md: Complete API documentation for all ETE classes and methods (Tree, PhyloTree, ClusterTree, NCBITaxa), including parameters, return types, and code examples
• workflows.md: Common workflow patterns organized by task (tree operations, phylogenetic analysis, tree comparison, taxonomy integration, clustering analysis)
• visualization.md: Comprehensive visualization guide covering TreeStyle, NodeStyle, Faces, layout functions, and advanced visualization techniques

Load these references when detailed information is needed:

# To use API reference
# Read references/api_reference.md for complete method signatures and parameters

# To implement workflows
# Read references/workflows.md for step-by-step workflow examples

# To create visualizations
# Read references/visualization.md for styling and rendering options

Troubleshooting

Import errors:

# If "ModuleNotFoundError: No module named 'ete3'"
uv pip install ete3

# For GUI and rendering issues
uv pip install ete3[gui]

Rendering issues:

If tree.render() or tree.show() fails with Qt-related errors, install system dependencies:

# macOS
brew install qt@5

# Ubuntu/Debian
sudo apt-get install python3-pyqt5 python3-pyqt5.qtsvg

NCBI Taxonomy database:

If database download fails or becomes corrupted:

from ete3 import NCBITaxa
ncbi = NCBITaxa()
ncbi.update_taxonomy_database()  # Redownload database

Memory issues with large trees:

For very large trees (>10,000 leaves), use iterators instead of list comprehensions:

# Memory-efficient iteration
for leaf in tree.iter_leaves():
    process(leaf)

# Instead of
for leaf in tree.get_leaves():  # Loads all into memory
    process(leaf)

Newick Format Reference

ETE supports multiple Newick format specifications (0-100):

• Format 0: Flexible with branch lengths (default)
• Format 1: With internal node names
• Format 2: With bootstrap/support values
• Format 5: Internal node names + branch lengths
• Format 8: All features (names, distances, support)
• Format 9: Leaf names only
• Format 100: Topology only

Specify format when reading/writing:

tree = Tree("tree.nw", format=1)
tree.write(outfile="output.nw", format=5)

NHX (New Hampshire eXtended) format preserves custom features:

tree.write(outfile="tree.nhx", features=["habitat", "temperature", "depth"])

Best Practices

1. Preserve branch lengths: Use preserve_branch_length=True when pruning for phylogenetic analysis
2. Cache content: Use get_cached_content() for repeated access to node contents on large trees
3. Use iterators: Employ iter_* methods for memory-efficient processing of large trees
4. Choose appropriate traversal: Postorder for bottom-up analysis, preorder for top-down
5. Validate monophyly: Always check returned clade type (monophyletic/paraphyletic/polyphyletic)
6. Vector formats for publication: Use PDF or SVG for publication figures (scalable, editable)
7. Interactive testing: Use tree.show() to test visualizations before rendering to file
8. PhyloTree for phylogenetics: Use PhyloTree class for gene trees and evolutionary analysis
9. Copy method selection: "newick" for speed, "cpickle" for full fidelity, "deepcopy" for complex objects
10. NCBI query caching: Store NCBI taxonomy query results to avoid repeated database access