# Central-position SNP distance (discussion)

Not implemented. Design discussion for a substitution-rate estimator that
observes SNPs directly from paired-genome k-mer comparison, as an alternative
to Mash's Poisson-Jaccard inference (see [obicompactvec](../implementation/obicompactvec.md)
for the implemented Jaccard/Mash distances).

## Motivation

Mash infers a mutation rate from a single scalar (Jaccard) via a model that
assumes independence across the k positions. For k odd, a substitution at the
**central base** of a k-mer, with the `2m` flanking bases (`m = (k-1)/2`)
otherwise conserved, is directly observable: it is a SNP. This gives access not
just to a rate but to the substitution's nature (transition/transversion),
enabling classical corrected distances.

## Statistic and correspondence with `shared`

A genomic position `p` is covered by `k` overlapping k-mer windows. Requiring
the substitution to sit at the window's **center** makes exactly one window
per SNP eligible — a 1:1 correspondence between SNP and center-neighbor k-mer
pair, avoiding the ~k-fold overcount of an any-position neighbor search.

A locus with a fully conserved `k`-window (flanks **and** center) is precisely
an exact-shared k-mer — already produced by the existing `shared_kmers` matrix
(`--shared-kmers`, `BitPartials::partial_jaccard` / `CountPartials::partial_threshold_jaccard`).
A locus with conserved flanks but a substituted center is a "central SNP". Both
count each locus exactly once, in matching units:

```
p_hat[i,j] = SNP[i,j] / (SNP[i,j] + shared[i,j])
```

`p_hat` is `P(center substituted | 2m flanks conserved)`. Only the numerator
`SNP[i,j]` needs computing; the denominator term is already available.

**Canonical invariance**: for odd k, the central position maps to itself under
reverse-complement (`m -> k-1-m = m`, base complemented). A transition maps to
a transition, a transversion to a transversion — the transition/transversion
split is well-defined in canonical space.

## Sufficient statistic: 4x4 base-pair tally

Tabulating the joint distribution of `(center_i, center_j)` over conserved-flank
loci, per genome pair, is sufficient for every downstream correction:

| Estimator | Input | Formula |
|---|---|---|
| Raw p-distance | total off-diagonal / total | `p = SNP / (SNP + shared)` |
| Jukes-Cantor | p | `d = -3/4 * ln(1 - 4p/3)` |
| Kimura 2-parameter | transition rate P, transversion rate Q | `d = 1/2 ln(1/(1-2P-Q)) + 1/4 ln(1/(1-2Q))` |
| LogDet/paralinear | full 4x4 + base-composition margins | `d ~= -1/4 ln det(F)`, robust to non-stationary base composition |

JC/K2P need only the total and the transition/transversion split. LogDet needs
the full 4x4 (adds a diagonal tally of shared k-mers' central base — cheap).

Memory for the 4x4 tally: `n^2 * 16` counters. Trivial for the project's
genome-scale use case (tens to hundreds of genomes); ~13 GB at n=10^4 — outside
scope but worth flagging if n grows.

## Biases (properties of the estimator, not defects)

1. **Conserved-flank ascertainment bias.** Only SNPs with intact `2m`-base
   flanks are visible; window-intact probability decays as `(1-p)^{2m}`. For
   k=31 (2m=30): 0.74 at p=1%, 0.21 at p=5%, 0.04 at p=10%. This estimator
   targets **closely related genomes**. Under rate heterogeneity across sites
   (universal in practice), conserved flanks correlate with slow centers, so
   `p_hat` underestimates the genome-wide average rate — it specifically
   estimates the substitution rate of **conserved regions**.
2. **Bias toward isolated SNPs.** Two SNPs within k of each other disqualify
   each other's flanks. Hypervariable regions are invisible by construction.
3. **Indels are invisible.** A frameshift destroys k-mer matches in a block;
   this channel captures substitutions only. Indel divergence shows up as lost
   shared k-mers (lower Jaccard/Mash), not as SNP signal.
4. **k-dependent specificity.** "A k-mer match implies common ancestry" is
   quantitative. For a 3 Gbp genome, expected random flank-30 collisions
   (k=31): `(3e9)^2 / 4^30 ~= 8` — negligible. At k=21: `(3e9)^2 / 4^20 ~= 2e6`
   — no longer negligible. k=31 is safe; k<=21 is marginal to unreliable for
   large genomes. The large k that guarantees homology is the same k that
   shrinks the detectable-divergence window — an inherent tension.

## Implementation: avoid materializing a de Bruijn graph

A central-SNP pair is topologically a simple bubble in the colored de Bruijn
graph (source/sink k-mer shared, two length-k branches differing only at the
midpoint). Classical bubble-calling (Cortex/discoSNP-style) finds these, but
requires the graph — nodes plus adjacency for ~10^9 colored k-mers — resident
in memory. **Rejected**: prohibitive RAM for this project's scale.

A naive per-pair generalisation of variant lookup across n genomes (query each
non-shared k-mer's 3 central variants against every counterpart genome's
index) costs `O(n^2 . N . 3)` random lookups, with the same k-mer's 3 variants
regenerated and requeried once per counterpart genome — pure redundant work.
**Rejected** as the basis for an n-genome design.

## Implementation: sequential per-partition sweep (no scratch, no graph)

`KmerIndex::distance()` already opens every partition's `presence_store`/
`count_store` simultaneously, memory-mapped, into one `LayeredStore`
(`distance.rs:73-77`). "Querying another partition" is therefore not a new
I/O pattern to design — it is the same O(1) MPHF+evidence lookup the `query`
command already performs at scale. This lets the SNP tally be computed with
**no scratch files and no auxiliary graph**, by sweeping partitions once each
as a source:

1. For source partition `p`, enumerate its **distinct** k-mers (one per MPHF
   slot; each already carries its full multi-genome presence/count vector —
   no need to explode per (k-mer, genome) occurrence).
2. For each, generate the 3 central-substitution variants and **canonicalise
   each independently** (`min(kmer, revcomp)`, exactly as any normal query) —
   this avoids the orientation edge case a masked-flank grouping would have
   (a substitution that flips canonical orientation is handled correctly
   because each variant is canonicalised on its own, not inferred from a
   fixed-orientation flank key).
3. Compute each variant's target partition `q` via its minimizer; batch/sort
   the partition's outgoing variant queries by `q` for locality.
4. Look up each variant in `q`'s already-mmap'd MPHF+evidence; on a hit,
   combine the source's presence vector (base `a`) with the variant's
   presence vector (base `b`): for every `i` carrying `a` and `j` carrying
   `b`, `tally[i,j][a,b] += 1`.

**Deduplication needs no persisted state.** Sweeping partitions in a fixed
order `p = 0, 1, ..., P-1` and only acting on a variant when its target
partition `q >= p` guarantees each unordered SNP pair is counted exactly
once: a pair with `q < p` was already resolved earlier, when `q` was itself
the source partition and `p` (being `>= q`) was a valid forward target. No
cross-partition flag array is needed — the sweep order *is* the
deduplication rule. Within the same partition (`q == p`), a lightweight
transient tie-break suffices: either a `#slots(p)`-bit scratch flag reset per
partition, or simply comparing the two k-mers' raw `u64` encodings and only
counting when `kmer_source < kmer_variant` — no storage at all.

This is a distinct computation stage, not a `partial_*` in the existing
additive-by-partition sense: step 3-4 read across partition boundaries by
construction, unlike the row-local `partial_jaccard`/`partial_threshold_jaccard`
primitives. But it requires no new index files, permanent or scratch:
`unitigs.bin`, `mphf.bin`, `evidence.bin`, and the presence/count columns are
read as-is, and the only extra memory is the current partition's small
outgoing-query batch (`#kmers(p) * 3`, released once `p` is done) plus the
persistent `tally` accumulator (`n^2 * 16` counters, see above).

**Outer loop (over source partitions `p`) must stay sequential.** Two
independent reasons, not just one: (a) memory — the bounded-footprint claim
above only holds with one partition's outgoing-query batch in flight; running
`T` source partitions concurrently multiplies that batch by `T`, exactly the
blowup the design avoids; (b) correctness — the `q >= p` deduplication rule
requires partitions to be claimed as sources in a fixed order; running `p1 <
p2` concurrently gives no guarantee `p1` has finished claiming its `q >= p1`
targets before `p2` starts claiming its own, breaking the "counted exactly
once" property.

**Inner loop (target-partition lookups for a fixed `p`) parallelises safely.**
Each lookup is an O(1) read against an already-mmap'd structure, independent
of the others, with no growing allocation — no memory blowup, no ordering
dependency between different `q`. The only shared mutable state is `tally`;
give each worker thread a **thread-local partial tally** (fixed `n^2 * 16`
size, independent of partition size) and merge into the global `tally` once
`p`'s inner loop completes — the same reduce-then-merge pattern Rayon already
uses elsewhere in this codebase to open partitions in parallel. Extra memory:
`#threads * n^2 * 16`, negligible (~512 MB at n~1000, 32 threads) and
unrelated to partition size.

**Cost**: `3 * N_distinct` MPHF lookups total across the whole index (each
partition swept once as source) — the same order of magnitude and the same
operation as running `query` over the index's entire k-mer content against
itself, three times. This is the tool's already-optimized regime, not a new
I/O profile to validate.

## Cheaper: subsampling

Since the target is a ratio, restricting the source-partition sweep to a
bottom-`s` hash sketch (only enumerate k-mers with `hash < threshold` as
sources) divides the lookup count by the sampling factor without biasing
`p_hat`. Mash-like tradeoff: rate estimated from a sample, not the full
k-mer set.

## Recommendation

Sequential per-partition sweep (Route D): reuse the already-mmap'd
per-partition MPHF/evidence/presence structures for O(1) variant lookups,
dedup via the fixed sweep-order rule (`q >= p`, plus an in-partition
tie-break), no scratch files, no graph materialisation. Denominator reused
from the existing `shared_kmers` matrix. Distances (p, JC, K2P, LogDet) as
finalisations of the resulting 4x4 tally, mirroring the `partial_* -> *_dist_matrix`
pattern used for Jaccard/Mash/Bray-Curtis/etc.

## Detailed implementation plan

Grounded in the current codebase. File/type references are anchors, not
prescriptions; adjust to reality when implementing.

### Step 0 — new low-level primitives (`obikseq`)

Two helpers do not yet exist and are prerequisites:

1. **Central neighbours.** `CanonicalKmerOf<L>` already exposes
   `left_canonical_neighbors()` / `right_canonical_neighbors()`
   (`obikseq/src/kmer.rs`), each returning the 4 canonicalised neighbours at
   an end position. Add `central_canonical_neighbors()` returning the 4
   variants at position `m = (k-1)/2` (each independently canonicalised via
   `.canonical()`). The 3 that differ from the source are the query variants;
   skip the identity. Building on `nucleotide(i)` / the raw 2-bit layout keeps
   it O(1).
2. **Lone-k-mer minimiser.** Routing a *synthetic* variant to its partition
   needs its minimiser, but `RollingStat` (`obiskbuilder/src/rolling_stat.rs`)
   only computes minimisers incrementally along a sequence. Add a standalone
   `minimizer(kmer) -> Minimizer` that scans the `k-m+1` m-mer windows
   (`PackedSeq::mmer`, `obikseq/src/packed_seq.rs`), canonicalises each, and
   takes the min by `seq_hash()` — the same selection `RollingStat` performs,
   evaluated once. Partition index is then
   `(minimizer.seq_hash() & (n_partitions - 1)) as usize`, exactly as
   `QueryBatch::from_records` (`obikmer/src/cmd/query.rs:142`); `n_partitions`
   is a power of two so the mask is valid.

### Step 1 — the tally accumulator (`obikindex`)

A `SnpTally` holding, per genome pair, the 4x4 joint count of central bases:
`n * n * 4 * 4` `u64` (or a packed lower-triangular form since it is
symmetric). Provide `merge(&mut self, other: &SnpTally)` for the thread-local
reduce, and accessors yielding, per pair `(i,j)`: total off-diagonal (SNP),
transition count `P`, transversion count `Q`. The diagonal need not be stored
for JC/K2P (the denominator comes from `shared_kmers`); store it only if
LogDet is wanted, by also tallying shared k-mers' central base.

### Step 2 — the sweep (`obikindex`, new `snp.rs`)

Mirror `distance.rs`: open the presence or count store per partition. But
instead of a per-partition `partial_*`, run the sequential source sweep:

```text
for p in 0..n_partitions:                       # OUTER — sequential
    open source partition p's layers (QueryLayer-style, obikpartitionner)
    enumerate distinct canonical k-mers of p (one per MPHF slot) with their
        presence/count vectors                   # column-major, as query stage 2
    par_iter over these source k-mers:           # INNER — rayon, thread-local tally
        for each of the 3 central variants:
            q = partition_of(variant)
            if q < p: continue                    # dedup: forward targets only
            if q == p and variant <= source.raw(): continue   # in-partition tie-break
            slot = layers[q].find_slot(variant)   # MphfLayer::find, mmap'd
            if hit:
                vb = variant presence/count vector
                for i in genomes with source base a:
                    for j in genomes with variant base b:
                        thread_tally[i,j][a,b] += 1
    merge thread-local tallies into global SnpTally
```

The inner lookup is precisely `QueryLayer::find_slot` +
`col_value(g, slot)` (`obikpartitionner/src/query_layer.rs`) — reuse or factor
out that path rather than reimplementing MPHF access. Enumerating "all distinct
k-mers of a partition with their vectors" is the `dump`/`query` stage-2
column-major scan already implemented in `dump_layer.rs` /
`query_partition_with`; factor a reusable iterator if none fits.

`presence_threshold` applies exactly as elsewhere: a genome "carries base b"
iff its count at that slot is `>= presence_threshold` (trivially `>= 1` for
presence indexes).

### Step 3 — finalisation (`obikindex`)

From the global `SnpTally` + the existing `shared_kmers` matrix, derive n x n
distance matrices, each a pure function of the accumulated counts (same shape
as `jaccard_to_mash`):

- `p_hat[i,j] = SNP / (SNP + shared)`
- Jukes-Cantor, Kimura-2P (from `P`, `Q`), optionally LogDet (needs the
  diagonal + base-composition margins).

Guard the singularities (`p >= 3/4` for JC, `1-2P-Q <= 0` or `1-2Q <= 0` for
K2P) by clamping to a max distance, as `jaccard_to_mash` clamps `J <= 0`.

### Step 4 — surfacing (`obikindex` + `obikmer` CLI)

These metrics do not fit `DistanceMetric`'s current `LayeredStore`-partial
dispatch (they need the cross-partition sweep and produce a different
intermediate). Two options, to decide:

- **(a)** New `DistanceMetric` variants (`Pdistance`, `JukesCantor`,
  `Kimura2P`, `LogDet`) whose `KmerIndex::distance` arm calls the sweep
  (`snp.rs`) instead of the partial path, still returning `DistanceOutput`.
  Keeps one CLI surface (`--metric jukes-cantor`), at the cost of a branch in
  `distance()` that ignores the `LayeredStore` it built.
- **(b)** A dedicated pathway (`KmerIndex::snp_distance`) and a distinct CLI
  entry, if mixing a cross-partition sweep into the partition-local `distance`
  command is judged architecturally muddy.

Recommendation: (a) for user ergonomics (all pairwise distances under
`distance`, all feeding NJ/UPGMA/`--shared-kmers` unchanged), but compute the
sweep lazily only when an SNP-family metric is requested, so the existing
metrics keep their partition-local fast path untouched.

### Step 5 — subsampling flag

Add `--snp-sample <fraction>` (or a bottom-`s` hash threshold): restrict the
source-k-mer enumeration in Step 2 to `seq_hash(kmer) < threshold`. Divides
lookups proportionally; `p_hat` is unbiased. Off by default (exact).

### Testing

- **Primitive unit tests**: `central_canonical_neighbors` on hand-checked
  k-mers incl. palindrome-boundary cases; lone-k-mer `minimizer` against
  `RollingStat`'s incremental result on the same k-mer.
- **End-to-end tiny index**: two 1-genome indexes differing by a handful of
  known isolated SNPs (transitions and transversions placed by hand), assert
  exact `SNP`, `P`, `Q` counts and the resulting JC/K2P values.
- **Dedup invariant**: assert the tally is identical regardless of genome/
  partition order and that no pair is double-counted (compare against a
  brute-force all-pairs reference on a small index).
- **Subsampling**: `p_hat` within sampling error of the exact run.

### Suggested phasing

1. Step 0 primitives + their unit tests (self-contained, no distance wiring).
   This also unblocks the long-declared-but-unimplemented `query --mismatch`
   (`obikmer/src/cmd/query.rs:676`, currently a warning), which needs the same
   neighbour + routing machinery.
2. `SnpTally` + finalisation math with a brute-force (non-swept) reference
   backend, validated on a tiny index.
3. The real per-partition sweep (Step 2) behind the same finalisation; assert
   it matches the brute-force backend.
4. CLI surfacing (Step 4a) and NJ/UPGMA integration (already generic over the
   matrix).
5. Subsampling (Step 5).

## References

The Mash mutation-rate model this discussion contrasts with:
[@Mash-distances-doc; @Fan2015-mash-formula].