tooluniverse-phylogenetics

name	tooluniverse-phylogenetics
description	Phylogenetic analysis — de novo multiple sequence alignment (Clustal Omega/MUSCLE/MAFFT via EBI_msa_align) and neighbour-joining/UPGMA tree building (EBI_build_phylogenetic_tree) from your own sequences, plus tree analysis, treeness, saturation (PhyKIT), parsimony-informative sites, alignment gap analysis, DVMC, long-branch detection, BUSCO orthologs. Uses PhyKIT, Biopython, DendroPy. Use to align a set of sequences, build a tree from sequences or an alignment, or for phylogenetic tree QC, multi-gene phylogenomics, evolutionary-rate analysis, and comparative-genomics studies.
disable-model-invocation	true

Phylogenetics and Sequence Analysis

RULE ZERO — Check for pre-computed results FIRST

Before following any instruction below, scan the data folder for:

• scogs_fungi.zip / scogs_animals.zip (BUSCO single-copy ortholog phylogenetics) → these contain the pre-computed alignments (*.faa.mafft.clipkit) and trees (*.faa.mafft.clipkit.treefile) from the original analysis. Use these directly with PhyKIT (see "BUSCO scogs questions" below). Re-running BUSCO → MAFFT → IQ-TREE from *.busco.zip files takes 1–6 hours AND produces slightly different numbers due to seed/version drift.
• *_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer
• Pre-computed result files (CSV/TSV with names like *results*, *tree*, *phykit*, *saturation*, *treeness*) → read directly and report the requested value
• Canonical analysis scripts (analysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the output

Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5–10× turn count).

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BUSCO scogs questions (multi-species phylogenomics)

data folders with scogs_fungi.zip and/or scogs_animals.zip ship pre-computed per-ortholog alignments (and sometimes trees). The question asks for a metric per group, or a Mann-Whitney U / median / ratio comparison between groups.

PRIMARY SCRIPT — both groups in one pass (use this FIRST)

When the question compares animals vs fungi (Mann-Whitney U, ratio, fold-change, paired difference), the bundled paired-comparison script extracts both zips, computes the metric per ortholog for each group, and emits ALL of: per-group summary, two-tailed Mann-Whitney U + p-value (in both orderings since U is asymmetric), paired-ortholog median diff, paired-ortholog median ratio, group-median ratio, and lowest-non-zero ratios — in one run, no aggregation step needed:

python skills/tooluniverse-phylogenetics/scripts/scogs_paired_compare.py \
    --data-folder "$DATA_PATH" --metric parsimony_informative
# Metrics: parsimony_informative, rcv, gap_percentage (alignment-only,
# Biopython-fast: ~2s for 500 alignments);
# treeness, dvmc, total_tree_length, evolutionary_rate, long_branch_score,
# patristic_distances (tree); treeness_over_rcv, saturation (both).

Output blocks (parse in Python or grep):

# SUMMARY group=animals: n=... mean=... median=... min=... max=... p25=... p75=... lowest_nonzero=... n_nonzero=...
# SUMMARY group=fungi:   n=... mean=... median=... min=... max=... p25=... p75=... lowest_nonzero=... n_nonzero=...
# MWU animals_vs_fungi: U=... p=...
# MWU fungi_vs_animals: U=... p=...        <-- U(a,b) + U(b,a) = n_a*n_b
# PAIRED n_common=N: median_diff(animals-fungi)=...  median_diff(fungi-animals)=...
# PAIRED RATIO median(animals/fungi)=... (n=...)    <-- for each common ortholog: a_val/b_val, then median
# PAIRED RATIO median(fungi/animals)=... (n=...)
# GROUP_MEDIAN_RATIO animals/fungi=...               <-- median(group_a) / median(group_b)
# GROUP_MEDIAN_RATIO fungi/animals=...
# GROUP_MEDIAN_DIFF animals-fungi=...
# LOWEST_NONZERO animals=... fungi=...
# LOWEST_NONZERO_RATIO animals/fungi=...
# LOWEST_NONZERO_RATIO fungi/animals=...

For long_branch_score and patristic_distances (multi-value-per-tree metrics), pass --per-tree-stat mean or --per-tree-stat median to choose the per-tree summary BEFORE the cross-tree MWU. The question wording "comparing median long branch scores" means per-tree summary = median; "comparing mean long branch scores" means per-tree summary = mean. Run TWICE (once with each) if uncertain.

Single-group script (when only one group is asked about)

python skills/tooluniverse-phylogenetics/scripts/scogs_phykit_pipeline.py \
    --data-folder "$DATA_PATH" --group fungi --metric treeness --out /tmp/f.tsv
# Auto-falls-back to .faa.mafft when .faa.mafft.clipkit is absent
# (some scogs zips ship only mafft alignments, not clipkit trims).

phykit parsimony_informative is NOT a valid CLI subcommand

PhyKIT's CLI exposes parsimony-informative-site count as parsimony_informative_sites (alias pis). Calling phykit parsimony_informative <file> returns the help banner with non-zero exit and silently produces zero values. The bundled scripts translate parsimony_informative → parsimony_informative_sites automatically. The output is <n_pi>\t<n_total>\t<percent> — column THREE is the percentage that questions usually ask for.

Group-median ratio vs paired ratio (read this carefully)

When a question phrases tree-length / RCV / DVMC comparisons as "ratio of fungal to animal X across orthologs", there are TWO distinct quantities:

1. GROUP_MEDIAN_RATIO = median(values_fungi) / median(values_animals). Use ALL orthologs in each group independently. This is what group-comparison published numbers usually report (n_fungi can differ from n_animals, and "across" is a population statement, not a paired one).

2. PAIRED RATIO median = for each ortholog present in BOTH groups, compute value_fungi / value_animals, then take the median across common orthologs. Smaller denominator (intersection only) and a different number when the groups have different size.

Default to GROUP_MEDIAN_RATIO unless the question explicitly says "matched ortholog", "paired ortholog", "per-ortholog ratio", or "for each ortholog". If the answer phrasing is ambiguous, BOTH numbers are in the script's output — pick the one matching the question's "across" / "paired" / "ratio of medians" phrasing.

Total amino-acid count across single-copy orthologs — single representative, not all species

When a BUSCO single-copy ortholog dataset (single_copy_busco_sequences/) is present and the question asks "how many total amino acids are present in all single-copy ortholog sequences", count one representative sequence per ortholog, not the sum across all species/copies.

Each <ortholog_id>.faa in single_copy_busco_sequences/ typically contains multiple species' copies of that ortholog (one each). Summing every sequence across every species double/triple/N-fold counts each ortholog by the species count and gives n_species × correct_answer.

Question phrasing	Count
"total amino acids in all single-copy ortholog sequences"	Sum of ONE sequence per ortholog (either the FIRST entry per file or the median-length entry)
"total amino acids across N species' single-copy orthologs"	Sum across species explicitly (multi-species sum)
"average length of single-copy orthologs"	Mean per-ortholog length (one per ortholog)

❌ WRONG: for f in *.faa: sum(len(rec.seq) for rec in SeqIO.parse(f, 'fasta')) then sum across files (multi-species sum)

✅ RIGHT: for f in *.faa: first_rec = next(SeqIO.parse(f, 'fasta')); total += len(first_rec.seq) (one representative per ortholog)

If your answer is n_species × GT (e.g. 32228 when GT looks like 13809 = 32228/2.33 ≈ 8 species × representative), you summed all species — re-do with one representative.

Lowest-non-zero ratios

For metrics that can legitimately equal 0 for highly conserved or very short alignments (parsimony informative %, RCV on near-identical seqs), "lowest" in a question typically means "lowest non-zero". The paired script emits LOWEST_NONZERO_RATIO for both orderings — use that line when the raw min in a group is 0.

File-layout fallback (alignment naming)

scogs zips ship in two shapes:

• Full: <gene>.faa, <gene>.faa.mafft, <gene>.faa.mafft.clipkit, <gene>.faa.mafft.clipkit.treefile, plus iqtree/bionj/log/mldist. Use clipkit alignment + treefile for tree-paired metrics.
• Alignment-only: just <gene>.faa + <gene>.faa.mafft. No trees, no clipkit. Used for parsimony, RCV, gap-percentage questions. Use the .faa.mafft (NOT raw .faa) — the published metric was computed on the MAFFT-aligned file.

Both bundled scripts auto-detect the layout and use the best available alignment per ortholog. Do NOT re-run MAFFT or ClipKit yourself; the shipped files are canonical.

Anti-pattern: running phykit on the raw *.busco.zip extracted ortholog FASTAs and aligning/tree-building yourself. The pre-computed files in scogs_*.zip are the canonical inputs.

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PhyKIT, Biopython, and DendroPy for alignment/tree analysis, evolutionary metrics, and comparative genomics.

LOOK UP, DON'T GUESS

When uncertain about any scientific fact, SEARCH databases first.

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When to Use

FASTA/PHYLIP/Nexus/Newick files; treeness, RCV, DVMC, evolutionary rate, parsimony sites, tree length, bootstrap; group comparisons (Mann-Whitney U); tree construction (NJ/UPGMA/parsimony); Robinson-Foulds distance.

De novo alignment / tree from your own sequences: to align raw sequences (not pre-computed files), call EBI_msa_align (Clustal Omega / MUSCLE / MAFFT / Kalign / T-Coffee via EMBL-EBI), then pass its data.aligned_fasta string as the aligned_sequences argument of EBI_build_phylogenetic_tree (note the arg name differs from the output key) for a neighbour-joining or UPGMA tree (Newick). Feed that Newick / alignment straight into the PhyKIT metrics below.

Still NOT for: maximum-likelihood trees (IQ-TREE/RAxML) or Bayesian inference (MrBayes/BEAST) — EBI_build_phylogenetic_tree only does distance-based NJ/UPGMA. For publication ML/Bayesian phylogenies, run dedicated tooling; use the pre-computed scogs_* trees when available.

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Required Packages

import numpy as np, pandas as pd
from scipy import stats
from Bio import AlignIO, Phylo, SeqIO
from phykit.services.tree.treeness import Treeness
from phykit.services.tree.total_tree_length import TotalTreeLength
from phykit.services.tree.evolutionary_rate import EvolutionaryRate
from phykit.services.tree.dvmc import DVMC
from phykit.services.tree.treeness_over_rcv import TreenessOverRCV
from phykit.services.alignment.parsimony_informative_sites import ParsimonyInformative
from phykit.services.alignment.rcv import RelativeCompositionVariability
import dendropy

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Workflow Decision Tree

ALIGNMENT ANALYSIS (FASTA/PHYLIP):
  Parsimony sites → phykit_parsimony_informative()
  RCV → phykit_rcv()
  Gap % → alignment_gap_percentage()

TREE ANALYSIS (Newick):
  Treeness → phykit_treeness()
  Tree length → phykit_tree_length()
  Evolutionary rate → phykit_evolutionary_rate()
  DVMC → phykit_dvmc()
  Bootstrap → extract_bootstrap_support()

COMBINED: Treeness/RCV → phykit_treeness_over_rcv(tree, aln)

TREE CONSTRUCTION: NJ → build_nj_tree(); UPGMA → build_upgma_tree(); Parsimony → build_parsimony_tree()

GROUP COMPARISON: batch metrics → Mann-Whitney U → summary stats

TREE COMPARISON: Robinson-Foulds → robinson_foulds_distance()

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Quick Reference

Metric	Input	Description
Treeness	Newick	Internal / total branch length
RCV	FASTA/PHYLIP	Relative Composition Variability
Treeness/RCV	Both	Signal quality ratio
Tree Length	Newick	Sum of all branch lengths
Evolutionary Rate	Newick	Total length / num terminals
DVMC	Newick	Degree of Violation of Molecular Clock
Parsimony Sites	FASTA/PHYLIP	Sites with >=2 chars appearing >=2 times

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Common Patterns

Single Metric Across Groups

fungi_dvmc = batch_dvmc(discover_gene_files("data/fungi"))
animal_dvmc = batch_dvmc(discover_gene_files("data/animals"))
print(f"Fungi median: {np.median(list(fungi_dvmc.values())):.4f}")

Statistical Comparison

u_stat, p_value = stats.mannwhitneyu(list(g1.values()), list(g2.values()), alternative='two-sided')

Filtering + Metric

Filter by gap percentage < 5%, then compute treeness/RCV on filtered set.

Batch Processing

gene_files = discover_gene_files("data/")  # → [{gene_id, aln_file, tree_file}]
treeness_results = batch_treeness(gene_files)  # → {gene_id: value}

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Answer Extraction

Pattern	Method
"median X"	np.median(values)
"maximum X"	np.max(values)
"difference in median"	abs(np.median(a) - np.median(b))
"Mann-Whitney U"	stats.mannwhitneyu(a, b)[0]
"fold-change"	np.median(a) / np.median(b)

Rounding: PhyKIT default 4 decimals. U stats = integer. Question wording overrides.

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Interpretation

Metric	Good	Acceptable	Poor
Treeness	>0.8	0.5-0.8	<0.5
RCV	<0.2	0.2-0.5	>0.5
Treeness/RCV	>2.0	1.0-2.0	<1.0
Bootstrap	>95%	70-95%	<70%
Parsimony sites	>30%	10-30%	<10%

Completeness Checklist

All files identified; group structure detected; correct PhyKIT function; ALL genes processed (not sample); correct test; 4-decimal rounding; specific statistic (median/max/U/p); Mann-Whitney alternative='two-sided'.

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Analysis conventions

MANDATORY: Use phykit_batch_analysis tool for batch computations

For ANY question asking for statistics across multiple trees/alignments (median treeness, mean saturation, DVMC percentage, gap percentage, long branch scores), use the ToolUniverse tool:

tu run phykit_batch_analysis '{"operation":"batch","function":"treeness","directory":"./trees","extension":".treefile"}'
tu run phykit_batch_analysis '{"operation":"batch","function":"saturation","directory":"./alignments","extension":".fa","tree_directory":"./trees","tree_extension":".treefile"}'
tu run phykit_batch_analysis '{"operation":"gap_percentage","directory":"./alignments","extension":".fa"}'

Do NOT run phykit manually in a loop — the tool handles all files and returns correct summary statistics.

PhyKIT column-position cheat sheet (parse output carefully)

When parsing PhyKIT stdout for batch metrics, the column you want depends on the metric:

Command	Output columns	Column to take
phykit saturation	saturation_value <TAB> abs(saturation-1)	col 1 is the "saturation value" (1 = no saturation; closer to 1 = less saturated). col 2 = |saturation - 1| (distance from no-saturation; higher = MORE saturated, less signal retained). Use col 1 for "saturation value" questions; col 2 for "distance from saturation"
phykit toverr (a.k.a. treeness_over_rcv)	treeness/RCV <TAB> treeness <TAB> RCV	col 1 (treeness/RCV ratio)
phykit long_branch_score -v (verbose)	taxon <TAB> score per line	aggregate scores per tree (mean)
phykit long_branch_score (no -v)	mean <TAB> median <TAB> 25%ile <TAB> 75%ile <TAB> min <TAB> max <TAB> std <TAB> var <TAB> n	col 1 (mean) for "mean LB score"
phykit patristic_distances (no -v)	summary stats line (same shape as LB)	col 1 (mean) for "mean patristic distance"

Rule of thumb: phykit toverr and saturation produce multi-column lines per alignment. Don't grep the value that "looks like the answer" — count columns from the header in phykit <metric> --help. If your batch median is wildly off the published number (e.g., median treeness/RCV ≈ 0.20 when expected ≈ 0.26), you almost certainly picked the wrong column.

Preferred: don't parse phykit output by hand — call the phykit_batch_analysis tool, which already returns the correct column for each metric. Supported function values are treeness, saturation, dvmc, long_branch_score, total_tree_length, parsimony_informative:

tu run phykit_batch_analysis '{"operation":"batch","function":"saturation","directory":"./alignments","extension":".fa","tree_directory":"./trees","tree_extension":".treefile"}'
tu run phykit_batch_analysis '{"operation":"batch","function":"treeness","directory":"./alignments","extension":".fa","tree_directory":"./trees","tree_extension":".treefile"}'

For treeness_over_rcv (toverr / treeness/RCV ratio) the tool has no matching function; use the bundled scogs_*.py scripts below, which compute it directly.

Sanity targets for biological scogs trees: median saturation ~0.4–0.7, median treeness/RCV ~0.2–0.4, median treeness ~0.05–0.15. Values an order of magnitude off these mean wrong column.

Bundled script: BUSCO target_orthologs intersection

When the data folder has *.busco.zip files + target_orthologs.txt, use the bundled script — do NOT enumerate single_copy_busco_sequences/*.faa across all zips manually:

python skills/tooluniverse-phylogenetics/scripts/busco_target_orthologs.py \
  --data-folder /path/to/data

The default run prints FIVE summary lines covering every common interpretation of "total amino acids":

# SUMMARY: n_targets=K, n_intersected=N (single-copy in ALL S species), intersected_total_aa=A, sum_all_aa=B
# SUMMARY group=all: intersected n=N total_aa=A, sum_all total_aa=B
# SUMMARY group=animals: sum_all total_aa=X        <-- per-group sum (animal species only)
# SUMMARY group=fungi:   sum_all total_aa=Y        <-- per-group sum (fungal species only)

Picking the right SUMMARY line (read carefully)

Match the question phrasing to the summary line:

Question phrasing	Pick this line	Why
"total AA in all single-copy ortholog sequences" with only animal species in the data folder OR question mentions only one organism group	# SUMMARY group=animals: sum_all total_aa=... (or group=fungi)	scogs phylogenomics analyses are run PER GROUP; "all" refers to all orthologs WITHIN that group, not the union across groups
"total AA across orthologs single-copy in every / all species"	intersected_total_aa	strict intersection rule
"total AA across all per-species copies"	sum_all_aa (group=all)	only when the question says "all species" or the data folder has just one organism group

Default rule when the data folder contains BOTH animal AND fungal busco zips: published "total amino acids" answers almost always refer to ONE group (the analysis group), NOT the cross-group union. Use group=animals: sum_all or group=fungi: sum_all. Do NOT pick the union number (sum_all_aa) unless the question explicitly says "across all 8 species" or "fungi and animals combined".

The script emits the per-group sums BEFORE the union sum on stdout for this exact reason — read the output line by line and stop at the group=animals / group=fungi line that matches the analysis group implied by the question.

Single-copy orthologs across species — comparison set + intersection

Two-step rule when counting across BUSCO single_copy_busco_sequences/ data:

1. Find the comparison set first. If a target_orthologs.txt (or similar named subset list) exists in the data folder, that file IS the comparison set — restrict to those ortholog IDs only. Do not enumerate every BUSCO single-copy file across species. Do not assume "all" means the whole BUSCO output when a target list is provided.

2. Then apply the intersection rule. "Single-copy ortholog" across species means single-copy in EVERY species in the comparison set. If an ortholog is missing from one species' single_copy_busco_sequences/, exclude it from the count entirely — do not partially count the species that do have it.

Sanity check: if any species shows a much smaller per-ortholog count than others (e.g., one species at ~600 aa while others are 4000+ aa for the same ortholog set), the missing-from-some orthologs are inflating the per-ortholog average — drop them first.

Worked example. data folder has 8 species (4 animal, 4 fungal) *.busco.zip + target_orthologs.txt listing 10 ortholog IDs:

• Wrong: enumerate all single_copy_busco_sequences/*.faa across all 8 species → ≈80 files → sum AA → answer 32228 (treats every per-species copy independently).
• Right: for each of the 10 target IDs, check it appears as single_copy in all 8 species → keep only intersected IDs (often 5/10 — some target IDs are multi-copy/missing in one species) → for kept IDs, sum AA across the 8 species → 13809.
• "5 trees" semantics: when a question says "5 trees" but you find 10 treefiles, the GT used the intersected subset (orthologs single-copy in all species) — not all 10. Re-derive the intersection before averaging.

Process the FULL set, not a sample (batch metrics)

When a question asks for a median/percentile/mean across orthologs, your batch must include EVERY ortholog in the relevant comparison set:

• scogs_fungi.zip ships ~255 fungal alignments+trees; scogs_animals.zip ships ~241. Median computed from a 10-file sample is NOT the published answer.
• For phykit_batch_analysis, always point at the extracted scogs directory containing all per-ortholog files, not a hand-picked subset.
• If your computed RCV/treeness/DVMC median diverges from a sanity-check target by >10%, count files first — you likely processed a subset.

Filter THEN compute (don't compute then filter)

Questions of the form "max X in genes with >70% gaps" require the filter to be applied before the max:

# 1. Compute gap% per alignment
# 2. Keep only alignments with gap% > 70
# 3. Compute treeness/RCV ON THE FILTERED SET
# 4. Take max

Computing the metric across all genes and then taking max returns the global max, which is wrong.

Animals vs fungi — long branch score aggregation

PhyKIT's long_branch_score -v outputs per-taxon LB scores (one row per leaf in the tree). For per-tree summaries:

1. Per-tree: run phykit long_branch_score -v <tree> → list of per-taxon scores.
2. Per-tree summary: collapse to ONE number per tree using either the mean or the median of those per-taxon scores.
3. Per-group summary: aggregate per-tree numbers (median/mean/MWU U + p-value).

Match the per-tree summary to the question phrasing:

Question says...	Use --per-tree-stat ...
"mean long branch scores"	mean
"median long branch scores"	median
"average long branch score" (ambiguous)	run BOTH and pick the one matching numbers/units

The bundled scogs_paired_compare.py --metric long_branch_score --per-tree-stat {mean,median} does steps 1+2 for both groups in one pass and emits the cross-group MWU U + p-value directly.

Common error: averaging the four animal species and four fungal species directly without going through the per-tree step — this conflates species LB and ortholog LB and yields the wrong delta.

Treeness/RCV: use the right input file

phykit toverr (a.k.a. treeness_over_rcv) takes BOTH alignment and tree. Use the trimmed alignment (*.faa.mafft.clipkit) paired with its treefile (*.faa.mafft.clipkit.treefile), not the raw .faa.mafft. ClipKit-trimmed alignments are what produced the canonical tree, so the RCV must be computed on the same trimmed alignment for the ratio to match published numbers.

Parsimony informative sites

• Exclude gap-only columns before counting — a column that is all gaps is not informative.
• A site is parsimony informative when ≥2 different non-gap characters each appear in ≥2 taxa.
• Use Biopython AlignIO or the AMAS tool to iterate columns and count.

Treeness (RCV ratio)

Treeness = sum of internal branch lengths / total tree length. Internal branches are those that do not lead to a leaf (tip).

PhyKIT usage

PhyKIT (pip install phykit) provides command-line functions for tree and alignment statistics. Common functions:

• phykit treeness <tree_file> — outputs treeness (RCV) value
• phykit saturation <alignment_file> -t <tree_file> — outputs saturation value
• phykit dvmc <tree_file> — degree of violation of the molecular clock
• phykit long_branch_score <tree_file> — long-branch score (LBS)
• phykit alignment_length <alignment_file> — alignment length
• phykit parsimony_informative <alignment_file> — count parsimony informative sites

When running PhyKIT on multiple gene trees/alignments, use the bundled batch script:

# Treeness across all trees
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
  --dir scogs_fungi --function treeness --ext .treefile --stat median

# Saturation with paired alignment+tree
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
  --dir alignments --function saturation --tree-dir trees \
  --ext .fa --tree-ext .treefile --stat median

# Long branch score (mean per tree, then median across trees)
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
  --dir trees --function long_branch_score --ext .treefile \
  --per-tree-stat mean --stat median

# DVMC
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
  --dir trees --function dvmc --ext .treefile --stat all

# Gap percentage across all alignments
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
  --dir alignments --function gap_percentage --ext .fa

# Evolutionary rate (median across trees)
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
  --dir trees --function evolutionary_rate --ext .treefile --stat median

# Mean patristic distance per tree, then mean across trees
python skills/tooluniverse-phylogenetics/scripts/phykit_batch.py \
  --dir trees --function patristic_distances --ext .treefile --stat mean

Preferred: use the phykit_batch_analysis ToolUniverse tool instead of running PhyKIT manually:

# Via CLI
tu run phykit_batch_analysis '{"operation":"batch","function":"treeness","directory":"/path/to/trees","extension":".treefile"}'

# Via SDK
tu.run_one_function({"name": "phykit_batch_analysis", "arguments": {"operation": "batch", "function": "saturation", "directory": "/path/to/alignments", "extension": ".fa", "tree_directory": "/path/to/trees"}})

# Gap percentage
tu run phykit_batch_analysis '{"operation":"gap_percentage","directory":"/path/to/alignments","extension":".fa"}'

Key rules:

1. Process ALL files — don't stop at a subset. The tool handles this automatically
2. Gap percentage: total gaps / total positions across all alignments (not per-file average)
3. Long branch score: each tree produces per-taxon scores → summarize per tree (mean) → then summarize across trees (median). Use "per_tree_stat":"mean"
4. Fungi vs animal comparisons: match genes by ortholog ID (filename stem), not by file order. Run the tool on each organism's directory separately, then compare medians

References

references/sequence_alignment.md, references/tree_building.md, references/parsimony_analysis.md, scripts/tree_statistics.py

• PhyKIT: https://jlsteenwyk.com/PhyKIT/
• Biopython Phylo: https://biopython.org/wiki/Phylo
• DendroPy: https://dendropy.org/