SPUMONI 2: Improved pangenome classification using a compressed index of minimizer digests
Bibliographic record
Abstract
Abstract Genomics analyses often use a large sequence collection as a reference, like a pangenome or taxonomic database. We previously described SPUMONI, which performs binary classification of nanopore reads using pangenomic matching statistics. Here we describe SPUMONI 2, an improved version that is faster, more memory efficient, works effectively for both short and long reads, and can solve multi-class classification problems with the aid of a novel sampled document array structure. By incorporating minimizers, SPUMONI 2 reduces index size by a factor of 2 compared to SPUMONI, yielding an index more than 65 times smaller than minimap2’s for a mock community pangenome. SPUMONI 2 also achieves a speed improvement of 3-fold compared to SPUMONI and 15-fold compared to minimap2. We show SPUMONI 2 achieves an advantageous mix of accuracy and efficiency for short and long reads, including in an adaptive sampling scenario. We further demonstrate that SPUMONI 2 can detect contaminated contigs in genome assemblies, and can perform multi-class metagenomic read classification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".