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

1. 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])

1. 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])

1. 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)

1. 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")

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