Etetoolkit Agent Skill

ETE Toolkit (ETE3): Python phylogenetic tree analysis and visualization. Parse Newick/NHX/PhyloXML, traverse/annotate nodes, render figures with TreeStyle/NodeStyle, integrate NCBI taxonomy, run PhyloTree comparative genomics. Use for species trees, gene family evolution, annotated tree figures.

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the whole folder, loaded on every use

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294

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Install

one command, takes just this skill from the repository

npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill etetoolkit

The instruction itself

27 sections, as written by the author

ETE Toolkit: Phylogenetic Tree Analysis and Visualization

Overview

ETE Toolkit (ETE3) is a Python framework for phylogenetic tree exploration, manipulation, and publication-quality visualization. It supports reading and writing Newick, NHX, PhyloXML, and NeXML formats, rich node annotation, programmatic tree traversal, NCBI taxonomy integration, and a flexible rendering engine for customizable tree figures. ETE3 is widely used in comparative genomics, phylogenomics, and evolutionary biology workflows.

When to Use

Parse phylogenetic trees from Newick, NHX, PhyloXML, or NeXML files and programmatically traverse or modify topology
Annotate tree nodes with metadata (bootstrap values, gene names, taxonomic ranks, expression data) for visualization or downstream analysis
Render publication-quality tree figures with custom node shapes, colors, branch widths, and face decorations using TreeStyle and NodeStyle
Map NCBI taxonomy IDs to lineage information, validate species names, or build taxonomy-aware trees
Compute evolutionary statistics: branch lengths, tree distances (Robinson-Foulds), LCA queries, monophyly tests
Build PhyloTree objects for comparative genomics — gene duplication/speciation event annotation, orthologs/paralogs inference
Prune, reroot, or ultrametricize trees programmatically before passing to downstream tools (BEAST, IQ-TREE, etc.)
For sequence alignment prior to tree building, use biopython-molecular-biology instead

Prerequisites

Python packages: ete3, numpy, PyQt5 (for interactive rendering), lxml (for PhyloXML)
Data requirements: Newick string or tree file; NCBI taxonomy database (downloaded on first use for NCBI module)
Environment: Python 3.6+; PyQt5 required for TreeStyle rendering and interactive GUI; headless rendering requires xvfb

> Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v python first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run python rather than bare python.

pip install ete3 numpy lxml PyQt5
# For headless rendering on Linux servers:
# apt-get install xvfb python3-pyqt5

Quick Start

from ete3 import Tree

# Load a Newick tree and inspect basic properties
t = Tree("((A:0.1,B:0.2)AB:0.3,(C:0.4,D:0.1)CD:0.2)root;")
print(f"Number of leaves: {len(t.get_leaves())}")
print(f"Leaf names: {t.get_leaf_names()}")
print(f"Tree depth: {t.get_farthest_leaf()[1]:.3f}")
# Number of leaves: 4
# Leaf names: ['A', 'B', 'C', 'D']
# Tree depth: 0.700
t.show()  # Opens interactive viewer (requires PyQt5)

Core API

Module 1: Tree I/O (Tree parsing and serialization)

Load trees from strings or files; write in various formats.

from ete3 import Tree, PhyloTree

# Parse Newick string (format 1 = standard Newick with support values)
t = Tree("((A:0.1,B:0.2)90:0.3,(C:0.4,D:0.1)85:0.2)root;", format=1)
print(f"Root children: {[n.name for n in t.children]}")

# Load from file
t_file = Tree("my_tree.nwk")

# Write Newick with internal names and supports
nwk_str = t.write(format=1)
print(f"Newick: {nwk_str}")

# Write to file
t.write(outfile="output_tree.nwk", format=0)
print("Saved output_tree.nwk")

from ete3 import PhyloTree

# Load PhyloXML tree (retains sequence annotations)
# pt = PhyloTree("my_phylo.xml", parser="phyloxml")

# Load NHX format (extended Newick with key=value annotations)
nhx = Tree("((A[&&NHX:S=human:D=Y],B[&&NHX:S=mouse:D=N]))")
for leaf in nhx.get_leaves():
    print(f"{leaf.name}: species={leaf.S}, duplication={leaf.D}")
# A: species=human, duplication=Y
# B: species=mouse, duplication=N

Module 2: Tree Traversal and Search

Navigate nodes using pre-order, post-order, or breadth-first traversal; search by name or attribute.

from ete3 import Tree

t = Tree("((Homo_sapiens:0.1,Pan_troglodytes:0.05)Hominidae:0.2,(Mus_musculus:0.3,Rattus_norvegicus:0.25)Muridae:0.4)Euarchontoglires;")

# Iterate all nodes (preorder by default)
for node in t.traverse("preorder"):
    depth = node.get_distance(t)
    print(f"{'leaf' if node.is_leaf() else 'internal'}: {node.name or 'unnamed'} depth={depth:.3f}")

# Search by name
human = t.search_nodes(name="Homo_sapiens")[0]
print(f"Human branch length: {human.dist:.3f}")
print(f"Ancestors: {[a.name for a in human.get_ancestors()]}")

from ete3 import Tree

t = Tree("((Homo_sapiens:0.1,Pan_troglodytes:0.05)Hominidae:0.2,(Mus_musculus:0.3,Rattus_norvegicus:0.25)Muridae:0.4)Euarchontoglires;")

# Lowest common ancestor (LCA) query
human = t & "Homo_sapiens"   # shorthand for search_nodes(name=...)[0]
mouse = t & "Mus_musculus"
lca = t.get_common_ancestor(human, mouse)
print(f"LCA of human and mouse: {lca.name}")
# LCA of human and mouse: Euarchontoglires

# Check monophyly
is_mono, mono_type, broken = t.check_monophyly(
    values=["Homo_sapiens", "Pan_troglodytes"], target_attr="name"
)
print(f"Hominids monophyletic: {is_mono}, type: {mono_type}")
# Hominids monophyletic: True, type: monophyletic

Module 3: Node Annotation

Add custom attributes to nodes for metadata-driven visualization and analysis.

from ete3 import Tree

t = Tree("((Homo_sapiens,Pan_troglodytes)Hominidae,(Mus_musculus,Rattus_norvegicus)Muridae)Euarchontoglires;")

# Annotate leaves with arbitrary metadata
metadata = {
    "Homo_sapiens":      {"genome_size_gb": 3.2, "ploidy": 2, "color": "blue"},
    "Pan_troglodytes":   {"genome_size_gb": 3.1, "ploidy": 2, "color": "green"},
    "Mus_musculus":      {"genome_size_gb": 2.7, "ploidy": 2, "color": "orange"},
    "Rattus_norvegicus": {"genome_size_gb": 2.9, "ploidy": 2, "color": "red"},
}
for leaf in t.get_leaves():
    for attr, val in metadata[leaf.name].items():
        setattr(leaf, attr, val)

# Access annotations
for leaf in t.get_leaves():
    print(f"{leaf.name}: {leaf.genome_size_gb} Gb, {leaf.color}")

from ete3 import Tree
import pandas as pd

t = Tree("((A,B)AB,(C,D)CD)root;")

# Load annotations from a DataFrame and apply to tree
df = pd.DataFrame({
    "name":  ["A", "B", "C", "D"],
    "value": [1.2, 3.4, 0.8, 2.1],
    "group": ["x", "x", "y", "y"],
})
name_to_row = df.set_index("name").to_dict(orient="index")

for leaf in t.get_leaves():
    if leaf.name in name_to_row:
        leaf.add_features(**name_to_row[leaf.name])
        print(f"{leaf.name}: value={leaf.value}, group={leaf.group}")

Module 4: Tree Manipulation

Prune, reroot, ultrametricize, and compute distances.

from ete3 import Tree

t = Tree("((A:0.1,B:0.2)AB:0.3,(C:0.4,D:0.1,(E:0.2,F:0.3)EF:0.1)CD:0.2)root;")
print(f"Original leaves: {t.get_leaf_names()}")

# Prune to a subset of taxa
t.prune(["A", "C", "E"], preserve_branch_length=True)
print(f"Pruned leaves: {t.get_leaf_names()}")

# Reroot on midpoint
t2 = Tree("((A:0.5,B:0.1):0.2,(C:0.3,D:0.4):0.1);")
midpoint_node, midpoint_dist = t2.get_midpoint_outgroup()
t2.set_outgroup(midpoint_node)
print(f"Rerooted at midpoint; root children: {[n.name for n in t2.children]}")

# Robinson-Foulds distance between two topologies
t_ref = Tree("((A,B),(C,D));")
t_alt = Tree("((A,C),(B,D));")
rf, rf_max, common_attrs, discard_t1, discard_t2, parts1, parts2 = t_ref.robinson_foulds(t_alt)
print(f"RF distance: {rf}, normalized: {rf/rf_max:.3f}")

Module 5: Tree Visualization (TreeStyle / NodeStyle)

Render publication-quality tree figures with custom styles.

from ete3 import Tree, TreeStyle, NodeStyle, faces, AttrFace, CircleFace

t = Tree("((Homo_sapiens,Pan_troglodytes)Hominidae,(Mus_musculus,Rattus_norvegicus)Muridae)Euarchontoglires;")

# Define node styles
for node in t.traverse():
    nstyle = NodeStyle()
    if node.is_leaf():
        nstyle["shape"] = "circle"
        nstyle["size"] = 8
        nstyle["fgcolor"] = "darkblue"
    else:
        nstyle["shape"] = "sphere"
        nstyle["size"] = 6
        nstyle["fgcolor"] = "gray"
    node.set_style(nstyle)

# Add text face to leaves
for leaf in t.get_leaves():
    name_face = AttrFace("name", fsize=12, fgcolor="black")
    leaf.add_face(name_face, column=0, position="branch-right")

# Configure TreeStyle
ts = TreeStyle()
ts.mode = "r"          # rectangular (use "c" for circular)
ts.show_leaf_name = False
ts.branch_vertical_margin = 15
ts.title.add_face(faces.TextFace("Phylogenetic Tree", fsize=16), column=0)

# Render to file (no display needed)
t.render("tree_figure.png", tree_style=ts, w=800, units="px")
print("Saved tree_figure.png")

from ete3 import Tree, TreeStyle, NodeStyle, faces, RectFace

t = Tree("((A,B)AB,(C,D)CD)root;")

# Circular cladogram with colored rectangles
metadata = {"A": "red", "B": "red", "C": "blue", "D": "blue"}
for leaf in t.get_leaves():
    leaf.color = metadata[leaf.name]
    leaf.add_face(RectFace(width=20, height=20, fgcolor=leaf.color, bgcolor=leaf.color),
                  column=0, position="aligned")

ts = TreeStyle()
ts.mode = "c"          # circular layout
ts.arc_start = -180
ts.arc_span = 359
ts.show_leaf_name = True

t.render("circular_tree.png", tree_style=ts, w=600, units="px")
print("Saved circular_tree.png")

Module 6: NCBI Taxonomy Integration

Map species to NCBI taxonomy, retrieve lineages, and build taxonomy trees.

from ete3 import NCBITaxa

# Initialize (downloads ~50 MB taxonomy DB on first call)
ncbi = NCBITaxa()
# ncbi.update_taxonomy_database()  # Refresh to latest NCBI taxonomy

# Name → taxid
taxid_map = ncbi.get_name_translator(["Homo sapiens", "Mus musculus", "Danio rerio"])
print(f"Taxid map: {taxid_map}")
# Taxid map: {'Homo sapiens': [9606], 'Mus musculus': [10090], 'Danio rerio': [7955]}

# Taxid → lineage
lineage = ncbi.get_lineage(9606)
names = ncbi.get_taxid_translator(lineage)
ranks = ncbi.get_rank(lineage)
for taxid in lineage[-6:]:
    print(f"  {ranks[taxid]:15s}: {names[taxid]}")

from ete3 import NCBITaxa, Tree, TreeStyle

ncbi = NCBITaxa()

# Build a taxonomy tree for a set of taxids
taxids = [9606, 10090, 7955, 6239, 7227]   # human, mouse, zebrafish, C. elegans, fruit fly
tree = ncbi.get_topology(taxids, intermediate_nodes=True)

# Annotate with common names
translator = ncbi.get_taxid_translator([int(n.name) for n in tree.get_leaves()])
for leaf in tree.get_leaves():
    leaf.sci_name = translator.get(int(leaf.name), leaf.name)
    print(f"Taxid {leaf.name}: {leaf.sci_name}")

# Render taxonomy tree
ts = TreeStyle()
ts.show_leaf_name = True
tree.render("taxonomy_tree.png", tree_style=ts, w=600, units="px")
print("Saved taxonomy_tree.png")

Module 7: PhyloTree for Comparative Genomics

Annotate gene trees with duplication/speciation events and query ortholog relationships.

from ete3 import PhyloTree

# Build a gene tree with species mapping
# Format: leaf names must follow "gene_SPECIES" or use sp_naming_function
nwk = "((Hsap_BRCA1:0.1,Ptro_BRCA1:0.05)0.99:0.2,(Mmus_Brca1:0.3,Rnor_Brca1:0.25)0.95:0.1);"
t = PhyloTree(nwk, sp_naming_function=lambda name: name.split("_")[0])

# Annotate events (duplication vs speciation)
t.get_descendant_evol_events()

# Report events per node
for node in t.traverse():
    if not node.is_leaf() and hasattr(node, "evoltype"):
        print(f"Node evoltype: {node.evoltype} "
              f"(D=duplication, S=speciation)")

# Get orthologs for a given leaf
leaf = t & "Hsap_BRCA1"
orthologs = leaf.get_sisters()
print(f"Orthologs of Hsap_BRCA1: {[n.name for n in t.get_leaves() if n != leaf]}")

Key Concepts

Newick Format Variants

ETE3's format parameter controls which Newick flavor to parse:

|--------|---------------------|----------------|-------------|

| 0 | Flexible | Yes | IQ-TREE, RAxML output |

| 1 | Named + support | Yes | Standard annotated trees |

| 5 | Internal names only | No | Topology-only trees |

| 9 | Leaf names only | Yes | Simple labeled trees |

| 100 | No names | No | Pure topology |

from ete3 import Tree
# RAxML output (bootstrap values as internal labels)
t = Tree("((A:0.1,B:0.2)90:0.3,(C:0.4,D:0.1)85:0.2);", format=1)
for node in t.traverse():
    if not node.is_leaf():
        print(f"Support: {node.support}, dist: {node.dist}")

Common Workflows

Workflow 1: Annotated Tree Figure from Alignment Output

Goal: Load an IQ-TREE result, annotate leaves with metadata, and render a publication figure.

from ete3 import Tree, TreeStyle, NodeStyle, AttrFace, faces
import pandas as pd

# Load IQ-TREE Newick output (format=1 handles support values)
t = Tree("iqtree_output.treefile", format=1)

# Midpoint rooting
outgroup, _ = t.get_midpoint_outgroup()
t.set_outgroup(outgroup)

# Load metadata table
meta = pd.read_csv("sample_metadata.csv")   # columns: name, clade, color
meta_dict = meta.set_index("name").to_dict(orient="index")

# Annotate leaves
for leaf in t.get_leaves():
    info = meta_dict.get(leaf.name, {})
    leaf.clade = info.get("clade", "unknown")
    leaf.face_color = info.get("color", "gray")

# Apply per-node styles
for node in t.traverse():
    ns = NodeStyle()
    if node.is_leaf():
        ns["size"] = 8
        ns["fgcolor"] = node.face_color if hasattr(node, "face_color") else "black"
    else:
        ns["size"] = 0   # hide internal nodes
    node.set_style(ns)

# Add name labels
for leaf in t.get_leaves():
    leaf.add_face(AttrFace("name", fsize=10), column=0, position="branch-right")
    leaf.add_face(AttrFace("clade", fsize=9, fgcolor="gray"), column=1, position="branch-right")

ts = TreeStyle()
ts.show_leaf_name = False
ts.branch_vertical_margin = 12
ts.scale = 200   # pixels per branch length unit

t.render("annotated_tree.pdf", tree_style=ts)
print("Saved annotated_tree.pdf")

Workflow 2: Taxonomy-Aware Species Tree Construction

Goal: Build a topology-correct species tree from a list of NCBI taxids and export as Newick.

from ete3 import NCBITaxa, TreeStyle

ncbi = NCBITaxa()

# Input: list of NCBI taxids
taxids = [9606, 10090, 7955, 6239, 7227, 3702]  # human, mouse, zebrafish, worm, fly, Arabidopsis

# Build topology tree
species_tree = ncbi.get_topology(taxids, intermediate_nodes=False)

# Translate taxid leaf names to scientific names
translator = ncbi.get_taxid_translator([int(n.name) for n in species_tree.get_leaves()])
for leaf in species_tree.get_leaves():
    leaf.name = translator.get(int(leaf.name), leaf.name)

# Export Newick
nwk = species_tree.write(format=9)
print(f"Species tree Newick:\n{nwk}")
with open("species_tree.nwk", "w") as f:
    f.write(nwk)

# Render figure
ts = TreeStyle()
ts.show_leaf_name = True
ts.mode = "r"
species_tree.render("species_tree.png", tree_style=ts, w=800, units="px")
print("Saved species_tree.nwk and species_tree.png")

Workflow 3: Batch Robinson-Foulds Comparison

Goal: Compare a set of bootstrap replicate trees against a reference topology.

from ete3 import Tree
import pandas as pd

# Reference tree
ref_tree = Tree("reference.nwk", format=1)
ref_leaves = set(ref_tree.get_leaf_names())

results = []
for i, line in enumerate(open("bootstrap_trees.nwk")):
    bt = Tree(line.strip(), format=1)
    # Prune to common taxa
    common = ref_leaves & set(bt.get_leaf_names())
    ref_pruned = ref_tree.copy()
    bt_pruned = bt.copy()
    ref_pruned.prune(list(common))
    bt_pruned.prune(list(common))

    rf, rf_max, *_ = ref_pruned.robinson_foulds(bt_pruned, unrooted_trees=True)
    norm_rf = rf / rf_max if rf_max > 0 else 0
    results.append({"replicate": i + 1, "rf": rf, "rf_max": rf_max, "norm_rf": norm_rf})

df = pd.DataFrame(results)
print(df.describe())
df.to_csv("rf_distances.csv", index=False)
print(f"Mean normalized RF: {df['norm_rf'].mean():.4f}")

Key Parameters

|-----------|--------|---------|-----------------|--------|

Common Recipes

Recipe: Extract All Subtrees Matching a Clade Name

When to use: Identify all subtrees rooted at nodes whose name matches a pattern.

from ete3 import Tree

t = Tree("((Homo_sapiens,Pan_troglodytes)Hominidae,(Mus_musculus,Rattus_norvegicus)Muridae)Euarchontoglires;")

target_clade = "Hominidae"
nodes = t.search_nodes(name=target_clade)
for node in nodes:
    print(f"Clade {node.name}: {node.get_leaf_names()}")
    subtree_nwk = node.write(format=1)
    print(f"  Newick: {subtree_nwk}")

Recipe: Ultrametricize a Non-Ultrametric Tree

When to use: Prepare a tree for time-calibrated analyses requiring ultrametric input.

from ete3 import Tree

t = Tree("((A:0.3,B:0.1):0.4,(C:0.7,D:0.2):0.1);")
print(f"Before: max leaf distance = {max(t.get_distance(l) for l in t.get_leaves()):.4f}")

# Convert to ultrametric using ETE's method
t.convert_to_ultrametric()
dists = [t.get_distance(l) for l in t.get_leaves()]
print(f"After: all leaf distances = {set(round(d, 6) for d in dists)}")

Recipe: Color Leaves by Group and Export SVG

When to use: Quickly produce a colored tree for presentations without manual styling.

from ete3 import Tree, TreeStyle, NodeStyle

t = Tree("((A,B,C)GroupX,(D,E)GroupY,(F,G,H)GroupZ);")
group_colors = {"GroupX": "steelblue", "GroupY": "tomato", "GroupZ": "seagreen"}

for node in t.traverse():
    ns = NodeStyle()
    if node.is_leaf():
        parent_name = node.up.name if node.up else ""
        ns["fgcolor"] = group_colors.get(parent_name, "black")
        ns["size"] = 10
    node.set_style(ns)

ts = TreeStyle()
ts.show_leaf_name = True
ts.branch_vertical_margin = 14
t.render("colored_tree.svg", tree_style=ts, w=600, units="px")
print("Saved colored_tree.svg")

Expected Outputs

Tree objects: Tree / PhyloTree instances — navigable node hierarchies with .children, .up, .name, .dist, .support
Newick strings: produced by t.write(format=N) — standard text for downstream tools
Image files: .png, .pdf, .svg via t.render() — resolution controlled by w parameter and dpi
Taxonomy trees: NCBITaxa.get_topology() returns a Tree with taxid node names
RF distances: integer raw RF + integer max RF from robinson_foulds(); normalize as rf / rf_max

Troubleshooting

| Problem | Cause | Solution |

|---------|-------|----------|

| NewickError: Unexepected char | Newick format mismatch (support vs name field) | Try format=0, 1, or 5; check if internal nodes have support values or names |

| RuntimeError: cannot connect to display | PyQt5 requires a display for rendering | Use t.render("out.png", ...) instead of t.show(); on headless servers, run under xvfb-run |

| AttributeError: 'Tree' object has no attribute | Node feature not annotated | Check dir(node) or use hasattr(node, "attr") before access |

| NCBI taxonomy DB not found | First-time use; DB not downloaded | Call NCBITaxa().update_taxonomy_database() once |

| Tree figure all leaves collapsed | Branch lengths all zero with scale set too low | Set ts.scale = None to auto-scale, or increase ts.scale value |

| ValueError on robinson_foulds | Trees have no common leaves | Prune both trees to shared taxon set before comparison |

| Slow rendering for large trees (500+ leaves) | Per-node Python rendering loop | Use ts.show_leaf_name = False and minimal faces; consider exporting Newick and rendering with FigTree |

References

ETE3 documentation — official tutorial covering all modules
ETE3 API reference — complete class and method reference
ETE3 GitHub repository — source, issue tracker, and example notebooks
Huerta-Cepas J, Serra F, Bork P (2016) ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. *Mol Biol Evol* 33:1635-1638 — primary citation for ETE3

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Take jaechang-hits/etetoolkit from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip, apt. Without those the skill loads but fails at the first command.