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.
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.
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).
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:
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:
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).
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.
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.
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.
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
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()
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'.
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.
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.
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:
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:
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.
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:
Per-tree: run phykit long_branch_score -v <tree> → list of
per-taxon scores.
Per-tree summary: collapse to ONE number per tree using either
the mean or the median of those per-taxon scores.
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
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:
Process ALL files — don't stop at a subset. The tool handles this automatically
Gap percentage: total gaps / total positions across all alignments (not per-file average)
Long branch score: each tree produces per-taxon scores → summarize per tree (mean) → then summarize across trees (median). Use "per_tree_stat":"mean"
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
