Evolution of the Largest Mammalian Genome
Bibliographic record
Abstract
The genome of the red vizcacha rat (Rodentia, Octodontidae, Tympanoctomys barrerae) is the largest of all mammals, and about double the size of their close relative, the mountain vizcacha rat Octomys mimax, even though the lineages that gave rise to these species diverged from each other only about 5 Ma. The mechanism for this rapid genome expansion is controversial, and hypothesized to be a consequence of whole genome duplication or accumulation of repetitive elements. To test these alternative but nonexclusive hypotheses, we gathered and evaluated evidence from whole transcriptome and whole genome sequences of T. barrerae and O. mimax. We recovered support for genome expansion due to accumulation of a diverse assemblage of repetitive elements, which represent about one half and one fifth of the genomes of T. barrerae and O. mimax, respectively, but we found no strong signal of whole genome duplication. In both species, repetitive sequences were rare in transcribed regions as compared with the rest of the genome, and mostly had no close match to annotated repetitive sequences from other rodents. These findings raise new questions about the genomic dynamics of these repetitive elements, their connection to widespread chromosomal fissions that occurred in the T. barrerae ancestor, and their fitness effects-including during the evolution of hypersaline dietary tolerance in T. barrerae.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".