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.
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## Input
- Query sequences in FASTA or FASTQ format (gzip supported, streaming stdin supported).
- 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).
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## Algorithm
The query follows the same superkmer-based partitioning strategy used at indexing time.
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.
Parallelism is **not yet active** in the current implementation: batches are processed sequentially on a single thread despite the `--threads` flag being parsed. The `QueryBatch` / `split_by_partition` design is structured to support per-partition parallelism in a future iteration.
`MphfLayer::open` reads `layer_meta.json` and loads either exact or approximate evidence. The caller (`QueryLayer::find`) never chooses the dispatch path — it is fixed at open time by `LayerEvidence`:
`find_exact` maps the k-mer through the MPHF to a slot, then calls `UnitigFileReader::verify_canonical_kmer(chunk_id, rank, kmer)` to confirm the stored k-mer matches. Zero false positives. Requires `UnitigFileReader::open()` (random-access via `.idx`); `open_sequential()` cannot serve random-access verification.
`find_approx` maps the k-mer through the MPHF, then checks a stored `b`-bit fingerprint of the canonical hash. False-positive rate: **1/2^b per k-mer query**. No `.idx` file is needed; the layer carries only `fingerprint.bin`.
For a query window of z consecutive k-mers (Findere scheme), the false-positive rate per window is **1/2^(b·z)**. The `z` parameter is recorded in `layer_meta.json` at build time but is not enforced during querying — the caller is responsible for interpreting window-level results accordingly.
`UnitigFileReader::open()` loads the `.idx` block-offset table, enabling random access to individual unitig chunks. It is required whenever `verify_canonical_kmer` is called (exact layers at query time).
`UnitigFileReader::open_sequential()` skips the `.idx` and supports only forward iteration. It is sufficient for:
- build passes that scan all unitigs sequentially (`build_exact_evidence`, `build_approx_evidence`);
- the `unitig` subcommand, which iterates and prints unitigs without random access.
`KmerIndex::open()` (called by `query::run`) triggers `MphfLayer::open` for each layer, which calls `UnitigFileReader::open()` for exact layers. Approximate layers do not open a unitig reader at all.
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## 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:
`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.
| `kmer_count` | int | always | k-mers with at least one match |
| `kmer_missing` | int | `--count-missing` | k-mers absent from every layer |
| `kmer_strict_matches` | object | always | per-genome accumulated value (label → count or 0/1) |
`kmer_count` counts matched k-mer positions (incremented once per `Some(row)` hit regardless of how many genomes are covered). `kmer_missing` counts `None` hits.
**Note on doc/impl divergence:** the doc previously used keys `kmer_total`, `kmer_found`, and `kmer_match` (list). The implementation uses `kmer_count` (int, matched only) and `kmer_strict_matches` (object keyed by genome label). `--mismatch` and `--detail` are parsed but not yet implemented and emit a warning.
