Eric Coissac
036d044291
Refactor `Kmer`, `SuperKmer`, and chunk reader into optimized, generic representations with compile-time length parameters and bitwise operations. Update the pipeline and scheduler to support batch processing, 1→N flat transformations, and multi-source merging. Introduce an approximate evidence mode using b-bit fingerprints and `.idx` files, alongside existing exact mode. Update CLI documentation, minimizer selection, and query output schema accordingly.
145 lines
6.6 KiB
Markdown
145 lines
6.6 KiB
Markdown
# Query system
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## Goal
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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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---
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## Input
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- Query sequences in FASTA or FASTQ format (gzip supported, streaming stdin supported).
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- Sequences shorter than k bases are silently skipped.
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- 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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---
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## Algorithm
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The query follows the same superkmer-based partitioning strategy used at indexing time.
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```
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for each batch of sequences:
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build QueryBatch: decompose all sequences into superkmers, deduplicate
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split superkmers by partition via minimiser hash
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for each partition p:
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query_partition(p, superkmers_routed_to_p)
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→ load QueryLayer(s) for p
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→ for each kmer in each superkmer: MphfLayer::find(kmer)
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broadcast results back to each (seq_idx, kmer_offset) that referenced the superkmer
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emit annotated sequences
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```
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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.
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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.
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---
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## Layer lookup: `MphfLayer::find`
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`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`:
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```rust
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pub fn find(&self, kmer: CanonicalKmer) -> Option<usize> {
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match &self.ev {
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LayerEvidence::Exact { .. } => self.find_exact(kmer),
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LayerEvidence::Approx { .. } => self.find_approx(kmer),
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}
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}
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```
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### Exact layers
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`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.
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### Approximate layers
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`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`.
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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.
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### `QueryLayer` variant selection
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`QueryLayer::open` in `query_layer.rs` selects the data matrix to pair with `MphfLayer`:
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| Condition | Variant | Data returned per k-mer |
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| `with_counts=true` and `counts/` exists | `Count` | raw count per genome |
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| `presence/` exists | `Presence` | 0/1 per genome (bit matrix) |
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| only `counts/` exists | `Count` | counts used as-is |
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| neither exists | `SetOnly` | 1 for every genome |
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---
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## `open()` vs `open_sequential()`
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`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).
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`UnitigFileReader::open_sequential()` skips the `.idx` and supports only forward iteration. It is sufficient for:
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- build passes that scan all unitigs sequentially (`build_exact_evidence`, `build_approx_evidence`);
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- the `unitig` subcommand, which iterates and prints unitigs without random access.
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`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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---
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## Presence / count mode at query time
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The `--force-presence` flag and `--presence-threshold` control how per-genome values are accumulated, independently of what the index stores:
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```
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genome_totals[g] += if presence { u32::from(v >= threshold) } else { v }
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```
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`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.
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---
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## Output format
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Output sequences are written in **OBITools4 format**: the original sequence with a JSON annotation map in the title line.
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```
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>read_id {"kmer_count":59,"kmer_strict_matches":{"genome_a":42,"genome_b":7,...}}
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ATCGATCG...
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```
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Genome keys in `kmer_strict_matches` are genome labels from `index.meta`. Key order follows iteration order of `meta.genomes`.
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---
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## Annotation schema (current implementation)
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| Key | Type | Condition | Semantics |
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|---|---|---|---|
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| `kmer_count` | int | always | k-mers with at least one match |
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| `kmer_missing` | int | `--count-missing` | k-mers absent from every layer |
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| `kmer_strict_matches` | object | always | per-genome accumulated value (label → count or 0/1) |
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`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.
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**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.
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---
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## CLI
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```
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obikmer query -i <index> [--detail] [--mismatch] [--count-missing]
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[--force-presence] [--presence-threshold <n>]
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[-T <threads>] <query.fa> [<query2.fa> ...]
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```
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`--mismatch` and `--detail` are accepted but currently ignored with a warning on stderr.
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---
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## Future work
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- **`--mismatch`**: 1-mismatch approximate matching — generate `3·k` single-substitution variants per k-mer, look each up independently.
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- **`--detail`**: per-position coverage vectors (`cov_<i>`) per genome.
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- **Read classification** (`--classify`): assign each read to the genome with the highest match score.
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- **Parallelism**: activate per-partition or per-sequence worker threads using the already-parsed `--threads` value.
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- **Whitelist / blacklist filtering**: threshold-based accept/reject on per-genome match scores.
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