MONI- <i>k</i> : An index for efficient pangenome-to-pangenome comparison
Bibliographic record
Abstract
Abstract Maximal exact matches (MEMs) are widely used in bioinformatics, originally for genome-to-genome comparison but especially for DNA alignment ever since Li (2013) presented BWA-MEM. Building on work by Bannai, Gagie and I (2018) and again targeting alignment, Rossi et al. (2022) recently built an index called MONI that is based on the run-length compressed Burrows-Wheeler Transform and can find MEMs efficiently with respect to pangenomes. In this paper we define k -MEMs to be maximal substrings of a pattern that each occur exactly at least k times in a text (so a MEM is a 1-MEM) and briefly explain why computing k -MEMs could be useful for pangenome-to-pangenome comparison. We then show that, when k is given at construction time, MONI can easily be extended to find k -MEMs efficiently as well.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".