etetoolkit

name	etetoolkit
description	Phylogenetic tree toolkit (ETE). Tree manipulation (Newick/NHX), evolutionary event detection, orthology/paralogy, NCBI taxonomy, visualization (PDF/SVG), for phylogenomics.

ETE Toolkit Skill

Overview

ETE (Environment for Tree Exploration) is a toolkit for phylogenetic and hierarchical tree analysis. Manipulate trees, analyze evolutionary events, visualize results, and integrate with biological databases for phylogenomic research and clustering analysis.

Core Capabilities

1. Tree Manipulation and Analysis

Load, manipulate, and analyze hierarchical tree structures with support for:

• Tree I/O: Read and write Newick, NHX, PhyloXML, and NeXML formats
• Tree traversal: Navigate trees using preorder, postorder, or levelorder strategies
• Topology modification: Prune, root, collapse nodes, resolve polytomies
• Distance calculations: Compute branch lengths and topological distances between nodes
• Tree comparison: Calculate Robinson-Foulds distances and identify topological differences

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 = {