kalarislabs/research-agent-skills/etetoolkit

ETE Toolkit 4

Scope

Use ETE 4 to work with an existing tree:

• Read Newick/Nexus, then inspect, annotate, transform, root, prune, and write

Newick trees

• Compare topologies and calculate phylogenetic distances
• Find repeated subtree topologies with TreePattern
• Analyze gene trees with PhyloTree
• Query local NCBI or GTDB taxonomy databases
• Explore large trees interactively with SmartView
• Render PNG with SmartView or PNG/PDF/SVG with the optional Qt treeview

ETE does not replace sequence alignment or phylogenetic inference software. For raw sequences, first use MAFFT or another aligner and IQ-TREE 2, FastTree, or another inference tool; then load the resulting tree into ETE.

Current Target

This skill targets ETE 4.4.0, released September 3, 2025 and verified as the current PyPI release on July 23, 2026.

Use https://etetoolkit.github.io/ete/ for ETE 4 documentation. The etetoolkit.org/docs/latest pages are legacy ETE 3 documentation despite the URL name.

Do not silently translate these examples back to ETE 3:

• Package and import: ete4, not ete3
• File input: pass an open file object; use strings for Newick text and do not

rely on path-string heuristics retained in ETE 4.4.0

• Newick selection: parser=, not format=
• Node metadata: props, add_prop(), and add_props()
• Iteration: leaves(), descendants(), and related methods return iterators
• Predicates: node.is_leaf and node.is_root are properties, not methods
• Node lookup: tree["name"], not tree & "name"

For porting older code, load `references/migration-ete3-to-ete4.md`.

Installation

Install the pinned base package:

uv pip install "ete4==4.4.0"

Add only the visualization extra required by the workflow:

# SmartView static PNG screenshots
uv pip install "ete4[render-sm]==4.4.0"
 
# Legacy Qt renderer for PNG, PDF, and SVG
uv pip install "ete4[treeview]==4.4.0"

Confirm the active environment:

uv run --with "ete4==4.4.0" python -c "import ete4; print(ete4.__version__)"

No credentials are required. NCBI and GTDB workflows download public taxonomy data and can consume substantial disk space; see `references/taxonomy.md` before the first update.

Quick Start

from pathlib import Path
from ete4 import Tree
# Use an open file object for files; reserve strings for Newick text.
with Path("tree.nw").open(encoding="utf-8") as handle:
    tree = Tree(handle, parser=1)  # parser 1: internal node names
print(tree.to_str(props=["name", "dist"], compact=True))
print("Leaves:", list(tree.leaf_names()))
# Search and annotate.
focal = tree["species1"]
focal.add_props(host="human", status="focal")
# Keep selected tips while preserving pairwise branch-length distances.
tree.prune(
    ["species1", "species2", "species3"],
    preserve_branch_length=True,
)
# Root and serialize explicitly.
tree.set_midpoint_outgroup()
tree.write(
    outfile="processed.nw",
    parser=1,
    props=["host", "status"],
)

Choose the parser deliberately. A parser mismatch is the most common cause of NewickError, lost internal labels, or support values being read as names. See `references/api_reference.md`.

Core Workflows

Inspect and transform a tree

from ete4 import Tree
tree = Tree("((A:1,B:1)CladeAB:0.4,C:2)Root;", parser=1)
for node in tree.traverse("preorder"):
    label = node.name if node.name is not None else node.id
    print(label, node.level, node.is_leaf, node.dist)
tree["A"].add_prop("group", "case")
tree["B"].add_prop("group", "control")
mrca = tree.common_ancestor("A", "B")
print(mrca.name)
tree.write(
    outfile="annotated.nhx",
    parser=1,
    props=["group"],
    format_root_node=True,
)

Node names need not be unique. tree["A"] returns the first match; use list(tree.search_nodes(name="A")) and validate the count when duplicates are possible.

Compare two topologies

from ete4 import Tree
tree_a = Tree("((A,B),(C,D));")
tree_b = Tree("((A,C),(B,D));")
(
    rf,
    max_rf,
    common_leaves,
    edges_a,
    edges_b,
    discarded_a,
    discarded_b,
) = tree_a.robinson_foulds(tree_b)
normalized_rf = rf / max_rf if max_rf else 0.0
print(rf, max_rf, normalized_rf, sorted(common_leaves))

RF comparison uses shared leaf labels and requires meaningful, preferably unique names. Decide explicitly whether rooted or unrooted comparison is scientifically appropriate.

Detect duplication and speciation events

from ete4 import PhyloTree
gene_tree = PhyloTree(
    "((Hsa|g1,Ptr|g1),(Hsa|g2,Mmu|g1));",
    sp_naming_function=lambda name: name.split("|", 1)[0],
)
for event in gene_tree.get_descendant_evol_events(sos_thr=0.0):
    relationship = "speciation/orthology" if event.etype == "S" else "duplication/paralogy"
    print(relationship, sorted(event.in_seqs), sorted(event.out_seqs))

Species-overlap calls are inferences from the supplied topology and naming function, not independent evidence of orthology. Pass the naming function explicitly, and use a rooted, fully bifurcating gene tree. For strict reconciliation, use a curated species tree and gene_tree.reconcile(species_tree).

Query taxonomy

from ete4 import NCBITaxa
ncbi = NCBITaxa()
names = ["Homo sapiens", "Pan troglodytes", "Mus musculus"]
name_to_taxids = ncbi.get_name_translator(names)
missing = [name for name in names if name not in name_to_taxids]
if missing:
    raise ValueError(f"Names not resolved by NCBI taxonomy: {missing}")
taxids = [name_to_taxids[name][0] for name in names]
taxonomy_tree = ncbi.get_topology(taxids)
print(taxonomy_tree.to_str(props=["sci_name", "rank"]))

ETE 4 also provides GTDBTaxa for genome-centric bacterial and archaeal taxonomy. Do not mix NCBI numeric TaxIDs and GTDB string identifiers.

Visualize

Interactive SmartView:

from ete4 import Tree
tree = Tree("((A:1,B:1)90:0.2,C:1);", parser="support")
tree.explore()

Static SmartView screenshot:

tree.render_sm("tree.png", w=1200, h=800)

render_sm() produces PNG screenshot data; use the Qt treeview renderer when the deliverable must be vector PDF or SVG. Load `references/visualization.md` for layouts, faces, remote exploration, and renderer selection.

Bundled Scripts

Run from this skill directory. The commands below use a pinned, isolated ETE 4 runtime through uv run --with.

Tree operations

uv run --with "ete4==4.4.0" python scripts/tree_operations.py \
  stats tree.nw --parser 1
uv run --with "ete4==4.4.0" python scripts/tree_operations.py \
  ascii tree.nw --parser 1 --props name,dist
uv run --with "ete4==4.4.0" python scripts/tree_operations.py \
  convert tree.nw output.nw \
  --input-parser 1 --output-parser 1
uv run --with "ete4==4.4.0" python scripts/tree_operations.py \
  reroot tree.nw rooted.nw \
  --parser 1 --midpoint
uv run --with "ete4==4.4.0" python scripts/tree_operations.py \
  prune tree.nw pruned.nw \
  --parser 1 --keep species1 species2 species3
uv run --with "ete4==4.4.0" python scripts/tree_operations.py \
  compare tree_a.nw tree_b.nw

Use --keep-file taxa.txt instead of --keep ... for one taxon per line. The script refuses ambiguous or missing requested names rather than silently producing a partial tree.

Visualization

# Interactive SmartView
uv run --with "ete4==4.4.0" python scripts/quick_visualize.py \
  tree.nw --parser 1
 
# SmartView PNG (requires ete4[render-sm])
uv run --with "ete4[render-sm]==4.4.0" python scripts/quick_visualize.py \
  tree.nw tree.png \
  --parser support --mode circular --show-support --color-by-support
 
# Vector output via Qt treeview (requires ete4[treeview])
uv run --with "ete4[treeview]==4.4.0" python scripts/quick_visualize.py \
  tree.nw tree.svg \
  --parser 1 --engine treeview --title "Species phylogeny"

Quality and Interpretation Checks

Before reporting a result:

1. Confirm the parser preserves the intended internal names, support, and

branch lengths.

1. Check for empty and duplicate leaf names before name-based lookup or RF

comparison.

1. State whether the tree is treated as rooted or unrooted.
2. Preserve branch lengths when pruning only if retained pairwise distances

should remain unchanged.

1. Treat arbitrary polytomy resolution as a display/algorithmic convenience,

not evolutionary evidence.

1. Record ETE version, parser, rooting method, pruning set, and taxonomy

database snapshot in reproducible analyses.

1. Prefer iterators for large trees and get_cached_content() for repeated

descendant-content queries.

Reference Map

Load only the reference needed for the task:

• `references/api_reference.md` — ETE 4 core

classes, parsers, properties, traversal, I/O, topology, and comparison

• `references/workflows.md` — complete analysis

patterns, validation, reconciliation, batching, and large-tree work

• `references/visualization.md` — SmartView,

layouts/faces, PNG screenshots, and Qt vector rendering

• `references/taxonomy.md` — NCBI and GTDB setup,

translation, topology, annotation, and reproducibility

• `references/migration-ete3-to-ete4.md`

— breaking API changes and porting checklist

Authoritative Upstream Sources

• Documentation: https://etetoolkit.github.io/ete/
• ETE 3 to ETE 4 migration: https://etetoolkit.github.io/ete/3to4.html
• Releases: https://github.com/etetoolkit/ete/releases
• PyPI: https://pypi.org/project/ete4/
• Source: https://github.com/etetoolkit/ete
• Visualization gallery: https://github.com/etetoolkit/ete-gallery

Agent operating procedure

1. Check the environment. Confirm tool versions, the reference genome/annotation build and the input formats (FASTQ, BAM, VCF, h5ad).
2. Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. Run a small version first. Run the pipeline on a small subset (one sample, one chromosome, a few thousand cells) first.
4. Execute the full task using the instructions and references above.
5. Validate the result. Check QC metrics, sample identities, genome build consistency and batch effects before interpreting results.
6. Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

Integrity rules

• Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
• Do not interpret biological significance beyond what the statistics support; report multiple-testing correction.
• Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
• Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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