chore: update ci cache, document distance metrics, and bump version #64

Merged
coissac merged 1 commits from push-wpxsvyylwmsq into main 2026-08-11 15:17:43 +00:00
4 changed files with 184 additions and 4 deletions
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@@ -25,8 +25,14 @@ jobs:
~/.cargo/registry ~/.cargo/registry
~/.cargo/git ~/.cargo/git
src/target src/target
key: ${{ runner.os }}-cargo-${{ hashFiles('src/Cargo.lock') }} # v2: bump this salt to force a clean cache when a stuck/killed job
restore-keys: ${{ runner.os }}-cargo- # may have saved a corrupted incremental-compilation `src/target`
# (observed 2026-08-11: a stale test binary deadlocked in NUMA
# worker startup; a from-scratch rebuild of the same source fixed
# it instantly, pointing at cache corruption rather than a source
# bug).
key: ${{ runner.os }}-cargo-v2-${{ hashFiles('src/Cargo.lock') }}
restore-keys: ${{ runner.os }}-cargo-v2-
- name: Build - name: Build
run: cargo build --release run: cargo build --release
+174
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@@ -203,6 +203,180 @@ implication is that columns should be allowed partial coverage (>=2 resolved
genomes, not unanimous) rather than requiring every genome to be net genomes, not unanimous) rather than requiring every genome to be net
single-copy at that locus. single-copy at that locus.
## Context, detectability, and a 3-way ordinal distance per pair
Empirical follow-up to the pseudo-alignment idea above: `obikmer distance
--snp` was run on a real 20-genome benchmark index and the resulting FASTA
fed to `raxml-ng`. Two problems surfaced, both traced back to conflating
distinct notions under one symbol.
**"Context", precisely.** Sharing a central base between two genomes is not
just sharing a nucleotide — it is sharing a **context**: the `2m` flanking
bases, identical, which is a homology claim about that flanked window
(guaranteed non-coincidental by k-specificity, Bias 4 above), *not* a claim
about orthology or paralogy of the copy each genome carries. `A` opposite `C`
= same context, divergent centre. `A` opposite nothing = **this context is
not observed in one of the two genomes** — informative, not neutral.
**Why the IUPAC/DNA encoding used for the first `--snp` test was wrong.**
Feeding IUPAC-coded ambiguity into a standard DNA model (`raxml-ng --model
GTR+G`) is a semantic mismatch: Felsenstein-pruning ML treats an ambiguous
tip as "exactly one true state, unknown which" (a uniform partial-likelihood
vector over compatible bases), not "these states are simultaneously
present". The two encodings look identical (same IUPAC letters) but the
software reads them backwards from what was intended — this invalidates the
literal branch lengths from that first experiment (topology-level groupings
by genus were still informative, see the worked example further down).
**Why `-` (absence) must not be scored as similarity, but also must not be
scored as a shared character between two absences.** Two genomes both
lacking a context are not observed to resemble each other at that locus —
neither is observed to differ from the other either. It is a symmetric
non-observation, uninformative for that pair, and should contribute nothing
(not a small positive nor a small negative signal) to their distance. A
genome carrying a state (`A`) against one carrying none is a different case
entirely: informative, and should not be scored as neutral "missing data"
the way a generic DNA/ML pipeline would.
**Detectability vs existence — a deliberate simplification, accepted.**
"Context not observed" conflates two different biological events: (1) true
loss of the locus, (2) the locus still exists but a mutation/indel *outside*
the centre, anywhere in the `2m` flanks, broke k-mer recognition. The design
adopts a rigorist stance on purpose: any flank-breaking mutation counts as
"this context no longer exists", full stop — because both causes (1) and (2)
independently require *at least* one more mutational event than a lone
central substitution would. This licenses treating "context absent in one of
the two genomes" as a **lower bound** on distance strictly greater than a
plain central SNP, without needing to know which of the two causes applies.
Coarser than a true event count, and accepted as such (fine substitution-type
modelling, e.g. transition/transversion weighting, is a secondary
refinement, not required for this to be useful).
**Resulting ordinal distance between two genomes at one family/context:**
| Comparison | Distance | Meaning |
|---|---|---|
| same centre (`A`/`A`) | `0` | identical |
| different centre, both single-copy (`A`/`C`) | `1` | plain central SNP |
| one genome has a state, the other has none | `>1` (lower bound) | context undetectable in one genome — at least one extra mutational event, of unknown type |
| neither genome has any state (`∅`/`∅`) | excluded | symmetric non-observation, not comparable, contributes nothing |
This is a direct extension of `KmerIndex::raw_snp_distance` (`obikindex/src/siblings.rs`),
which today only implements the `0`/`1` rows and silently drops everything
else (including the informative `>1` row) rather than scoring it.
**Open, not yet resolved:**
- Calibrating `>1` to a real number for tools expecting continuous distances
(NJ/UPGMA, ML branch lengths), rather than an arbitrary placeholder.
Natural route: estimate `p_hat` from the resolved (`0`/`1`) sites first,
then use the already-derived ascertainment formula (`P(usable window
showing a central SNP) = p * (1-p)^(2m)`, Bias 1 above) to derive a
model-consistent value for the `>1` bucket instead of guessing a constant.
- Where multi-copy/ambiguous states (the IUPAC case: a genome carrying more
than one form) fit into this ordinal scheme — plausibly also `>1` by the
same "at least one extra event" argument (a second form appearing is a
gain, itself an event), but not yet worked out.
**Practical alternative validated for the pseudo-alignment output itself**
(orthogonal to the ordinal-distance question above, useful regardless of how
`>1` ends up calibrated): re-encode each family as 4 independent binary
presence/absence characters (`A`,`C`,`G`,`T` columns) instead of one IUPAC
column, feed to a `BIN`-type model instead of `DNA`. `∅` becomes an explicit
`0000` state (identity with another `0000`, not missing data) rather than a
gap — removes the semantic mismatch above by construction. Known cost,
accepted for now: a plain central substitution (`A` -> `C`) becomes 2 binary
flips (`1000` -> `0100`), overweighting substitutions relative to true
gain/loss events, and the 4 sub-characters of one family are not
statistically independent the way a generic `BIN` model assumes. A proper
fix (single 16-state alphabet, i.e. the powerset of `{A,C,G,T}`, with a
substitution-rate structure that respects the subset lattice rather than a
fully general 16x16 GTR-analogue) is very likely not expressible in
`raxml-ng`'s `MULTI` datatype as-is (Mk or fully-general rates only) and a
fully general 16-state rate matrix is almost certainly unidentifiable here
(states of cardinality >=3 are ~2% of sites in the benchmark run). Treated as
a longer-term research question, not a near-term implementation target.
## Sankoff parsimony as the resolution of the 16-state model problem
The "longer-term research question" just above (a 16-state alphabet — the
powerset of `{A,C,G,T}` — with a substitution structure that respects the
subset lattice) turns out to have a near-term answer, once the *unification*
question below is worked through.
**Distance methods (NJ/UPGMA/ME) vs. character methods (parsimony/ML): not a
deep philosophical divide, but a real practical distinction for this
project.** Historically "phenetic" (characters -> distances -> tree) and
"cladistic" (characters -> tree directly) approaches were presented as
opposed schools; the modern view is a mathematical continuity, not a
dichotomy — Minimum Evolution (ME: find the tree minimizing total branch
length from a distance matrix) and Maximum Parsimony (MP: find the tree
minimizing total character-state changes) are both instances of "minimise a
global explanatory cost", and coincide under simple encodings (see Farris
1983, "The Logical Basis of Phylogenetic Analysis"; the MP/ME connection is
developed in the Minimum Evolution / Balanced Minimum Evolution literature,
e.g. Nei and colleagues — citations not independently re-verified here, flag
before quoting further). NJ's own agglomeration step already uses the whole
distance matrix jointly (the Q-matrix), not just the pair being merged — an
earlier claim in this discussion that distance methods are "blind" to
cross-taxon structure at every stage was too strong.
What *does* remain a real, structural distinction for this project: in a
character method, a given character's cost is **re-evaluated per candidate
topology** during tree search (the same family can cost 1 change under one
topology, 2 under another). In a pairwise-distance pipeline
(`raw_snp_distance` as it exists today), each family's contribution to
`d(i,j)` is computed **once**, independent of any candidate topology, before
NJ/UPGMA ever runs — so a question like "does this shared `∅` look like a
synapomorphy under topology T" can never be posed in that pipeline, for any
T. That question is only answerable by a method that tests candidate
topologies and re-scores characters under each — i.e. a character method.
**Sankoff parsimony directly resolves the `∅`/gain-loss/substitution
question, without the identifiability problem of a fitted 16-state model.**
Sankoff's algorithm generalises Fitch parsimony to an arbitrary
user-supplied cost matrix between states (`obikseq`/`obikindex` would treat
each family as a `2^{4}`-state character, state = subset of `{A,C,G,T}`
observed in that genome, `∅` included as a real state, not a gap). The
previous 16-state idea failed specifically because *fitting* a full 16x16
rate matrix by ML is unidentifiable at this data volume; Sankoff sidesteps
that because the cost matrix is **fixed a priori from domain knowledge**, not
estimated — e.g. `c({A},{C}) = 1` (a substitution), `c({A},{A,C}) = 1` (a
gain), `c({A,C},{A}) = 1` (a loss), `c({A,C},{G,T}) = 2` (two changes) — no
estimation, no overparameterisation. This reframes gain/loss and central
substitution as two cost categories with independently chosen weights,
exactly the "two families of parameters" (`mu_substitution`, `mu_gain/loss`)
floated earlier in this discussion, now with an actual algorithmic home.
**Caveat, not blocking for this project's scope.** Sankoff is still
parsimony: in principle exposed to Felsenstein's statistical-inconsistency
result under long-branch attraction (already invoked earlier against
`D_F = min(a,b)`) — parsimony and ML only provably coincide in the
short-branch regime. This is not a practical concern here because it is
exactly this estimator's declared target (closely related genomes, short
branches) — the regime where parsimony's known failure mode does not apply —
but worth stating explicitly as a scope guard rather than leaving it
implicit.
**Cheapest next experiment: don't write a Sankoff tree-search engine, use
one that exists.** The hard part of a from-scratch implementation is not the
Sankoff DP itself (a straightforward dynamic program over a *fixed* tree) but
the topology search (SPR/NNI with incremental re-scoring) that comes for
free with `raxml-ng` on the ML side. **TNT** (Tree analysis using New
Technology, free, standard in morphological cladistics) already implements
Sankoff parsimony with a custom cost matrix plus topology search — the
family-state matrix (already close to what `--snp` produces, minus the
IUPAC/DNA-model mismatch) could be fed there directly, no new code required,
before considering a bespoke engine.
**The concrete comparison this unlocks:** run both pipelines on the same
family data —
`k-mer families -> D_ij -> NJ/ME/UPGMA` (phenetic, what exists today) vs.
`k-mer families -> characters -> argmin_T Sankoff-cost(T)` (cladistic, via
TNT) — and compare the resulting topologies. Agreement would validate that
the pairwise-distance projection preserves the phylogenetic signal;
disagreement would pinpoint exactly what the projection to a single number
per pair loses. Not yet run.
## Heterozygosity, ploidy, and consensus-assembly inputs ## Heterozygosity, ploidy, and consensus-assembly inputs
A within-genome multiplicity signal (more than one of the 4 central forms A within-genome multiplicity signal (more than one of the 4 central forms
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@@ -1715,7 +1715,7 @@ dependencies = [
[[package]] [[package]]
name = "obikmer" name = "obikmer"
version = "1.1.41" version = "1.1.42"
dependencies = [ dependencies = [
"clap", "clap",
"csv", "csv",
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@@ -1,6 +1,6 @@
[package] [package]
name = "obikmer" name = "obikmer"
version = "1.1.41" version = "1.1.42"
edition = "2024" edition = "2024"
[[bin]] [[bin]]