tooluniverse-phylogenetics

# Phylogenetics and Sequence Analysis ## Four traps that produce a confidently wrong number Each of these was observed producing a wrong answer *while the correct guidance was already present further down this file*. Check them before you answer. 1. **PhyKIT prints more than one column, and for `saturation` the two conventions disagree — state which you used.** `phykit saturation` prints `saturation <TAB> |saturation-1|`. Its own `--help` is explicit: *"The first value is the saturation value and the second column is the absolute value of saturation minus 1."* But several published analyses (and some reference answers derived from them) report the **second** column as "the saturation value". The two always sum to 1.0000, which is the tell that you may be looking at the wrong one — on the fungal scogs set the medians are 0.39 (col 1) and 0.61 (col 2). So: **follow phykit and use column 1** unless the question or source defines saturation the other way, and say in your answer which column you read. Do not silently pick the one that looks closer to an expected number. `treeness_over_rcv` has no such ambiguity: it gives `ratio <TAB> treeness <TAB> RCV` and the ratio is first. 2. **"Gap percentage" means the fraction of alignment COLUMNS containing at least one gap**, not the fraction of residues that are gaps. On the fungal scogs set the residue definition maxes out at 0.556, so a ">70% gaps" filter selects **nothing** and the question looks unanswerable; by columns, three orthologs qualify (max 0.783). 3. **`treeness_over_rcv` and `rcv` take the UNTRIMMED `.faa.mafft`**, while `saturation` takes the trimmed `.clipkit`. RCV measures variability across columns, so trimming changes it: median 0.2683 untrimmed against 0.3050 trimmed, and among >70%-gap genes the maximum is 0.2572 untrimmed against 0.4174 trimmed. 4. **Never loop PhyKIT per file.** `phykit_batch_analysis` is parallel and does ~250 trees in about 35 seconds; a shell loop takes ~9 minutes and runs out of turns mid-way, producing no answer at all. It also selects the right column for every function, which removes trap 1 entirely. ## 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). --- ## 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: ```bash python skills/tooluniverse-phylogenetics/scripts/scogs_paired_compare.py \ --data-folder "$DATA_PATH" --metric parsimony_informative # Metrics: parsimony_informative, rcv, gap_percentage (alignment-only, # Biopython-fast: ~2s for 500 alignments); # treeness, dvmc, total_tree_length, evolutionary_rate, long_branch_score, # patristic_distances (tree); treeness_over_rcv, saturation (both). ``` Output blocks (parse in Python or grep): ``` # SUMMARY group=animals: n=... mean=... median=... min=... max=... p25=... p75=... lowest_nonzero=... n_nonzero=... # SUMMARY group=fungi: n=... mean=... median=... min=... max=... p25=... p75=... lowest_nonzero=... n_nonzero=... # MWU animals_vs_fungi: U=... p=... # MWU fungi_vs_animals: U=... p=... <-- U(a,b) + U(b,a) = n_a*n_b # PAIRED n_common=N: median_diff(animals-fungi)=... median_diff(fungi-animals)=... # PAIRED RATIO median(animals/fungi)=... (n=...) <-- for each common ortholog: a_val/b_val, then median # PAIRED RATIO median(fungi/animals)=... (n=...) # GROUP_MEDIAN_RATIO animals/fungi=... <-- median(group_a) / median(group_b) # GROUP_MEDIAN_RATIO fungi/animals=... # GROUP_MEDIAN_DIFF animals-fungi=... # LOWEST_NONZERO animals=... fungi=... # LOWEST_NONZERO_RATIO animals/fungi=... # LOWEST_NONZERO_RATIO fungi/animals=... ``` For `long_branch_score` and `patristic_distances` (multi-value-per-tree metrics), pass `--per-tree-stat mean` or `--per-tree-stat median` to choose the per-tree summary BEFORE the cross-tree MWU. The question wording "comparing **median** long branch scores" means per-tree summary = median; "comparing **mean** long branch scores" means per-tree summary = mean. Run TWICE (once with each) if uncertain. ### Single-group script (when only one group is asked about) ```bash python skills/tooluniverse-phylogenetics/scripts/scogs_phykit_pipeline.py \ --data-folder "$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. - **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. #### Which alignment goes with which metric (this changes the answer) The tree is always the ClipKit-derived `.faa.mafft.clipkit.treefile`. The **alignment** argument depends on the metric: | metric | alignment to pass | |---|---| | `treeness`, `dvmc`, `total_tree_length`, `long_branch_score` | tree only — no alignment | | `saturation` | `.faa.mafft.clipkit` (trimmed) | | **`treeness_over_rcv` / `rcv`** | **`.faa.mafft` (untrimmed)** | | parsimony-informative sites, gap percentage | `.faa.mafft` (untrimmed) | RCV measures compositional variability **across the alignment's columns**, so trimming changes it materially — and `treeness_over_rcv` divides by RCV, so the trimmed alignment shifts the ratio for every gene. Verified on the fungal scogs set (249 orthologs, canonical shipped files): ``` median treeness/RCV untrimmed .faa.mafft = 0.2683 trimmed .clipkit = 0.3050 max treeness/RCV (over the 3 genes with >70% gapped columns: 1260807at2759 0.0861, 1567796at2759 0.1866, 939345at2759 0.2572) untrimmed .faa.mafft = 0.2572 trimmed .clipkit = 0.4174 ``` Plain `treeness` needs no alignment and is unaffected — it reproduces exactly (median 0.0501 on the same 249 files), which is how the alignment choice was isolated as the cause rather than the tree set or the tool. `phykit_batch_analysis` takes the two independently, so pass them explicitly: ```bash tu run phykit_batch_analysis '{"operation":"batch","function":"treeness_over_rcv", "directory":"<dir>","extension":".faa.mafft", "tree_directory":"<dir>","tree_extension":".faa.mafft.clipkit.treefile"}' ``` **Gap percentage** in these questions means the fraction of alignment **columns containing at least one gap**, not the fraction of all residues that are gaps. The two differ by an order of magnitude: with the residue definition no fungal ortholog exceeds 70% gaps, so a ">70% gaps" filter silently selects nothing. **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 ```python 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() ``` --- ## Quick Reference | Metric | Input | Description |