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# Query system

## Goal

Given a set of query sequences, determine for each sequence how many of its k-mers are found in the index and, for each indexed genome, how many k-mers match. The query system is the foundation for read classification and sequence-to-genome mapping.

## Input

- Query sequences in FASTA or FASTQ format (gzip supported, streaming stdin supported). GenBank flat files are not supported at query time (only at index time).
- Sequences shorter than k bases are silently skipped.
- Non-ACGT characters are handled by the superkmer decomposition layer: they act as hard breaks, producing shorter superkmers (identical to the behaviour at indexing time).

## Algorithm

The query follows the same superkmer-based partitioning strategy used at indexing time.

    for each chunk of sequences (parallel workers via obipipeline):
        build QueryBatch: decompose all sequences into s-mers via superkmers, deduplicate
        allocate seq_results[seq_idx][smer_pos] = None  ← per-sequence s-mer result vectors
        split superkmers by partition via minimiser hash
        for each partition p:
            query_partition(p, superkmers_routed_to_p)
                → load QueryLayer(s) for p
                → for each s-mer in each superkmer: MphfLayer::find(smer)
            fill seq_results[seq_idx][kmer_offset + j] from partition results
        for each sequence:
            apply_findere(seq_results[seq_idx], effective_z)  ← per full sequence
            accumulate confirmed k-mer results into acc and cov
        emit annotated sequences

Superkmers that appear more than once in the batch (same sequence or across sequences) are deduplicated: each unique `RoutableSuperKmer` is queried once per partition, and the result is broadcast to every `SKDesc` entry that references it.

Findere requires full-sequence aggregation. `apply_findere` is applied once per sequence on the complete s-mer result vector, after all partitions have contributed. Applying it per superkmer would produce false negatives at superkmer boundaries, where the z-window spans two superkmers.

Batches are processed in parallel via `obipipeline` workers; the `--threads` flag controls the number of worker threads.

## Findere z-window filter

For approximate index modes, the index physically stores s-mers of size `s = k_user − z + 1`. At query time, `set_k(s)` is in effect, so queries naturally produce s-mer results. `apply_findere` then aggregates z consecutive s-mer results into one k_user-mer answer:

    fn apply_findere(
        results: &[Option<Box<[u32]>>],  // N s-mer results
        z: usize,
        n_genomes: usize,
    ) -> Vec<Option<Box<[u32]>>>         // N − z + 1 k_user-mer results

Input length N (s-mers), output length N − z + 1 (k_user-mers).

For each genome g independently, a sliding window of size z scans the input. Output position i is confirmed for genome g iff all z values `results[i..i+z][g]` are nonzero (`None` counts as zero for all genomes). The scan is O(n) per genome.

Output values come from `results[i]` (leftmost s-mer of each window); genomes not confirmed are zeroed. If all genomes are zero, the position is returned as `None`.

Short sequences: when the s-mer count is less than z, no complete window can form — `apply_findere` returns an empty vector. K-mers from sequences shorter than k_user are not emitted.

Exact indexes: `z = 1`, `apply_findere` is a passthrough (output length = input length).

### Effective z at query time

`effective_z` is resolved at the start of `run()`:

    let effective_z = args.findere_z.unwrap_or_else(|| match idx.meta().config.evidence {
        IndexMode::Approx { z, .. } | IndexMode::Hybrid { z, .. } => z as usize,
        IndexMode::Exact => 1,
    });

The `-z` CLI option overrides the index metadata value. A higher z increases stringency (lower FP, some true positives may be discarded at sequence ends); a lower z increases sensitivity.

## Layer lookup: `MphfLayer::find`

`MphfLayer::open(dir, mode: &IndexMode)` receives the mode from `PartitionMeta` — no per-layer file is read. The caller (`QueryLayer`) never chooses the dispatch path: it is fixed at open time by `LayerEvidence`. See obilayeredmap for the full `find` / `find_strict` API.

### `QueryLayer` variant selection

`QueryLayer::open` in `query_layer.rs` selects the data matrix to pair with `MphfLayer`:

Condition | Variant | Data returned per k-mer
--- | --- | ---
`with_counts=true` and `counts/` exists | `Count` | raw count per genome
`presence/` exists | `Presence` | 0/1 per genome (bit matrix)
only `counts/` exists | `Count` | counts used as-is
neither exists | `SetOnly` | 1 for every genome

## Presence / count mode at query time

The `--force-presence` flag and `--presence-threshold` control how per-genome values are accumulated, independently of what the index stores:

    genome_totals[g] += if presence { u32::from(v >= threshold) } else { v }

`presence` is true when `--force-presence` is set or when the index has no counts (`!with_counts`). The default `presence_threshold` is 1, so any nonzero count counts as a match.

## Coverage vectors (`--detail`)

When `--detail` is requested, a 3-D accumulator `cov[seq_idx][genome][kmer_pos]` is allocated after all partitions are queried, with dimensions derived from `n_kmers_out = n_smers − z + 1` (k_user-mer positions, not s-mer positions):

    cov[seq_idx][g][pos] += contribution
    where pos is the k_user-mer index in the filtered (post-Findere) vector

Coverage reflects confirmed k_user-mers only. The vectors are emitted in the JSON annotation under the key `"coverage"`.

## `kmer_missing` semantics

`kmer_missing` counts k_user-mer positions where the first s-mer (`seq_results[seq_idx][pos]`) is `None` — i.e. absent from the index entirely. K-mers where the z-window fails because a later s-mer is absent or zero are not counted as missing (the first s-mer being present is used as proxy for index membership).

## Output format

Output sequences are written in OBITools4 format: the original sequence with a JSON annotation map in the title line.

    >read_id {"kmer_count":59,"kmer_strict_matches":{"genome_a":42,"genome_b":7}}
    ATCGATCG...

With `--detail`:

    >read_id {"kmer_count":59,"kmer_strict_matches":{...},"coverage":{"genome_a":[0,1,2,...],...}}
    ATCGATCG...

Genome keys follow the iteration order of `meta.genomes`.

## Annotation schema

Key | Type | Condition | Semantics
--- | --- | --- | ---
`kmer_count` | int | always | k-mers confirmed (post-Findere) with at least one genome match
`kmer_missing` | int | `--count-missing` | k-mers absent from the index entirely (pre-Findere None)
`kmer_strict_matches` | object | always | per-genome accumulated value (label → count or 0/1)
`coverage` | object | `--detail` | per-genome array of per-position contributions (label → [u32])

`kmer_count + kmer_missing` ≤ total k_user-mers in the sequence. The gap corresponds to k_user-mers whose z-window was not fully confirmed (at least one s-mer absent or zero for all genomes) but whose first s-mer was present in the index.

## CLI

    obikmer query <index> [--detail] [--mismatch] [--count-missing]
                  [--force-presence] [--presence-threshold <n>]
                  [-z <z>] [-T <threads>]
                  <query.fa> [<query2.fa> ...]

Option | Default | Semantics
--- | --- | ---
`-z` / `--findere-z` | from index metadata | Override Findere z parameter
`--detail` | off | Emit per-position coverage vectors in JSON
`--count-missing` | off | Add `kmer_missing` field to JSON
`--force-presence` | off | Report 0/1 per genome regardless of index counts
`--presence-threshold` | 1 | Minimum count to declare genome present
`-T` / `--threads` | all CPUs | Worker threads

`--mismatch` is accepted but currently ignored with a warning on stderr.

## Future work

- `--mismatch`: 1-mismatch approximate matching — generate `3·k` single-substitution variants per k-mer, look each up independently.
- Read classification (`--classify`): assign each read to the genome with the highest match score.
- Whitelist / blacklist filtering: threshold-based accept/reject on per-genome match scores.