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