Phylogenetics

When to use

Use this workflow for an alignment of homologous loci or proteins, a gene tree, branch support, and an interpretable tree figure. Multi-locus partitioning, concordance factors, ancestral states and dating are covered in IQ-TREE reference.

A gene tree does not establish a species tree, transmission direction, or horizontal transfer by itself. Sampling dates alone do not justify a molecular clock.

Versions and installation

Reviewed against current official documentation on 2026-10-01. The executed smoke workflow used MAFFT 7.526, IQ-TREE 3.1.4, FastTree 2.2.0 and ETE3 3.1.3 on Python 3.12. ETE3 remains deliberate for this helper; ETE4 is a separate API and is not a drop-in replacement. ETE3 imports cgi, removed in Python 3.13. The native executables are not Python packages.

Illustrative installation commands; verify executable versions after installation:

conda create -n phylo -c conda-forge -c bioconda mafft iqtree fasttree trimal
conda activate phylo
uv venv --python 3.12 .venv-phylo
uv pip install --python .venv-phylo/bin/python ete3==3.1.3 numpy six
# Optional rendering backend; it can require a working Qt platform plugin.
uv pip install --python .venv-phylo/bin/python PyQt5

The helper defaults to iqtree3 and FastTree; use --iqtree-bin iqtree2 for an existing IQ-TREE 2 installation, or --fasttree-bin fasttree if that is the installed name. Version 2 compatibility is source-checked, not runtime-tested in this refresh. All analyses are local; this skill invokes no HTTP API or remote inference endpoint.

Workflow

1. Establish the biological input

• Select homologs with appropriate taxon coverage; investigate paralogs, contamination and recombination before assuming one history describes all sites.
• Use unique, whitespace-free FASTA IDs and keep a separate sample metadata table. The helper rejects empty/all-missing records and Newick punctuation in IDs.
• Specify --type nt or --type aa. The helper sends -st DNA or -st AA to IQ-TREE and selects nucleotide/protein FastTree flags explicitly.
• Coding DNA needs codon-aware alignment if using a codon model. Ordinary MAFFT nucleotide alignment does not preserve reading frames automatically.
• Keep the original sequences, accession versions, filters, alignment and exact commands. Inspect alignment lengths, gaps, composition and taxon identities.

2. Align and inspect

These MAFFT commands were exercised on small synthetic FASTA data:

mafft --auto --thread 1 --inputorder sequences.fasta > aligned.fasta
# L-INS-i: locally alignable homologs, typically small datasets.
mafft --localpair --maxiterate 1000 --thread 1 --inputorder sequences.fasta > linsi.fasta
# E-INS-i: homologs with large internal gaps.
mafft --genafpair --maxiterate 1000 --thread 1 --inputorder sequences.fasta > einsi.fasta
# FFT-NS-i (two refinement cycles), then FFT-NS-2 (progressive).
mafft --retree 2 --maxiterate 2 --thread 1 --inputorder sequences.fasta > fftnsi.fasta
mafft --retree 2 --maxiterate 0 --thread 1 --inputorder sequences.fasta > fftns.fasta

auto delegates the choice to MAFFT. Sequence count alone is not an accuracy criterion. Single-thread runs simplify reproducibility; MAFFT iterative refinement with multiple threads can produce different results between runs.

Trimming is optional and must be justified by alignment quality, not assumed to improve every tree. The following trimAl command is documentation-checked and illustrative; trimAl was not installed for this review:

trimal -in aligned.fasta -out trimmed.fasta -automated1 -fasta

Record retained columns/taxa and compare sensitivity to trimming. A failed trim must stop the trimming step; do not silently copy the untrimmed input under a “trimmed” filename. The bundled helper does not trim automatically.

3. Infer a tree

Use the maintained helper instead of recreating subprocess wrappers. From the skill directory, with the executable names on PATH:

python scripts/phylogenetic_analysis.py sequences.fasta --type nt --threads 1 \
  --bootstrap 1000 --seed 42 --output-dir results --no-visualization
# Use a reviewed alignment (including any explicit trimming) without realigning.
python scripts/phylogenetic_analysis.py aligned.fasta --aligned --type nt \
  --threads 1 --output-dir reviewed --no-visualization
# Protein FastTree alternative; --threads applies to MAFFT/IQ-TREE, not FastTree.
python scripts/phylogenetic_analysis.py proteins.fasta --type aa --fasttree \
  --threads 1 --output-dir fast_protein --no-visualization

The standard IQ-TREE command uses -m MFP (ModelFinder including FreeRate models), -B 1000 (UFBoot) and --alrt 1000. -m TEST remains supported but searches a narrower model set. Use --model for a scientifically justified fixed model. IQ-TREE checkpoints are preserved; --redo explicitly restarts and replaces results. Use a new output prefix when data or model assumptions change.

FastTree uses -nt -gtr -gamma for DNA and -lg -gamma for proteins. Its search uses the CAT approximation before Gamma20 rescaling; it is not a full Gamma-model ML search. JTT is FastTree's protein default (no -jtt flag); nucleotide JC is selected by -nt without -gtr. FastTree defaults to SH-like local support in the 0–1 range, not UFBoot percentages. Use workload-based benchmarking, not a universal taxon threshold, to decide between inference tools.

4. Interpret support, rooting and outputs

Read .iqtree and .log, not only the image. In the default combined test, IQ-TREE writes slash-delimited SH-aLRT/UFBoot labels; verify their order in the report. UFBoot >=95 and SH-aLRT >=80 are common screening thresholds, not proof of correctness or posterior probabilities. Do not apply a standard-bootstrap 70% rule to UFBoot. Model misspecification, alignment error and sampling can produce strongly supported wrong branches; consider -bnni when assessing model violations and UFBoot overestimation.

Reversible substitution models do not infer the evolutionary root. The helper preserves the supplied tree orientation unless an explicit --outgroup or --midpoint display choice is made. Select outgroups biologically; midpoint rooting assumes sufficiently clock-like path lengths and is only a heuristic. --outgroup passes one taxon to IQ-TREE and verifies that it exists. Display rerooting preserves support labels on their splits. Neither display option creates a dated phylogeny or overwrites the original inferred tree.

Primary outputs are <prefix>.treefile (IQ-TREE) or <prefix>.tree (FastTree), the alignment, and IQ-TREE's .iqtree, .log, .ckp.gz, plus optional support files. .model.gz caches model-selection work; it is not an estimated model parameter report. Keep these artifacts together.

5. Summarize and visualize

ETE must read combined IQ-TREE labels as internal names with format=1; its default numeric-support parser cannot parse 95.2/99. The helper renders the original text labels, not ETE's default support value. Branch statistics use all original non-root edges, including zero-length edges, without rerooting.

# Run from the skill directory in the ETE3 environment.
import sys
sys.path.insert(0, "scripts")
from phylogenetic_analysis import load_tree, tree_summary

tree = load_tree("results/sequences.treefile")
print(tree.get_leaf_names())
print(tree_summary("results/sequences.treefile"))

To request rendering, omit --no-visualization. ETE3 needs PyQt5; on a headless host the appropriate Qt platform plugin must be available. Missing rendering support leaves the inferred tree intact and is reported explicitly. Qt rendering was not exercised in this review. FigTree or iTOL can also display Newick; verify that a viewer retains the support labels and branch-length scale.

Official references

• MAFFT manual and threading
• IQ-TREE quickstart, current releases, model assumptions
• FastTree manual
• ETE3 tree parsing and drawing
• trimAl manual