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
tree = Tree("tree.nw", format=1)
print(f"Leaves: {len(tree)}")
print(f"Total nodes: {len(list(tree.traverse()))}")
taxa_to_keep = ["species1", "species2", "species3"]
tree.prune(taxa_to_keep, preserve_branch_length=True)
midpoint = tree.get_midpoint_outgroup()
tree.set_outgroup(midpoint)
tree.write(outfile="rooted_tree.nw")
Use scripts/tree_operations.py for command-line tree manipulation:
python scripts/tree_operations.py stats tree.nw
python scripts/tree_operations.py convert tree.nw output.nw --in-format 0 --out-format 1
python scripts/tree_operations.py reroot tree.nw rooted.nw --midpoint
python scripts/tree_operations.py prune tree.nw pruned.nw --keep-taxa "sp1,sp2,sp3"
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
tree = PhyloTree("gene_tree.nw", alignment="alignment.fasta")
def get_species(gene_name):
return gene_name.split("_")[0]
tree.set_species_naming_function(get_species)
events = tree.get_descendant_evol_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}")
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:
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()
species = ["Homo sapiens", "Pan troglodytes", "Mus musculus"]
name2taxid = ncbi.get_name_translator(species)
taxids = [name2taxid[sp][0] for sp in species]
tree = ncbi.get_topology(taxids)
for node in tree.traverse():
if hasattr(node, "sci_name"):
print(f"{node.sci_name} - Rank: {node.rank} - TaxID: {node.taxid}")
Annotating existing trees:
for leaf in tree:
species = extract_species_from_name(leaf.name)
taxid = ncbi.get_name_translator([species])[species][0]
lineage = ncbi.get_lineage(taxid)
ranks = ncbi.get_rank(lineage)
names = ncbi.get_taxid_translator(lineage)
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")
ts = TreeStyle()
ts.show_leaf_name = True
ts.show_branch_support = True
ts.scale = 50
