Simulated assemblies for "Multi-genome synteny detection using minimizer graph mappings"
Bibliographic record
Abstract
ntSynt is a multi-genome synteny detection tool that utilizes lightweight minimizer graphs to simultaneously map multiple genomes to one another. We tested ntSynt on multiple ~3 Gbp genomes, and show that it computes high-quality macrosynteny blocks quickly and with a low memory footprint. Here, we provide human assemblies with simulated rearrangements at various variant rates. The T2T human reference genome was rearranged in 4 separate runs using SURVIVOR, and SNVs (single nucleotide variants) and indels (small insertions and deletions) were introduced at various rates using pIRS. SURVIVOR was configured to simulate two translocations between 10–50 kbp, 20 inversions between 10–50 kbp and 20 indels between 50–100 kbp. Each gzipped tarball provided contains the 4 rearranged human genomes at the different SNV (-s) and indel (-d) rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".