The genome-wide rate and spectrum of spontaneous mutations differ between haploid and diploid yeast
Bibliographic record
Abstract
Characterizing the number, types, locations, and effects of thousands of mutations revealed that haploids were more prone to single-nucleotide mutations (SNMs) and mitochondrial mutations, while larger structural changes were more common in diploids. Mutations were more likely to be detrimental in diploids, even after accounting for the large impact of structural changes, contrary to the prediction that mutations would have weaker effects, due to masking, in diploids. Haploidy is expected to reduce the opportunity for conservative DNA repair involving homologous chromosomes, increasing the insertion-deletion rate, but we found little support for this idea. Instead, haploids were more susceptible to SNMs in late-replicating genomic regions, resulting in a ploidy difference in the spectrum of substitutions. In diploids, we detect mutation rate variation among chromosomes in association with centromere location, a finding that is supported by published polymorphism data. Diploids are not simply doubled haploids; instead, our results predict that the spectrum of spontaneous mutations will substantially shape the dynamics of genome evolution in haploid and diploid populations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".