Do larger genomes contain more diverse transposable elements?
Bibliographic record
Abstract
BACKGROUND: The genomes of eukaryotes vary enormously in size, with much of this diversity driven by differences in the abundances of transposable elements (TEs). There is also substantial structural and phylogenetic diversity among TEs, such that they can be classified into distinct classes, superfamilies, and families. Possible relationships between TE diversity (and not just abundance) and genome size have not been investigated to date, though there are reasons to expect either a positive or a negative correlation. This study compares data from 257 species of animals, plants, fungi, and "protists" to determine whether TE diversity at the superfamily level is related to genome size. RESULTS: No simple relationship was found between TE diversity and genome size. There is no significant correlation across all eukaryotes, but there is a positive correlation for genomes below 500 Mbp and a negative correlation among land plants. No relationships were found across animals or within vertebrates. Some TE superfamilies tend to be present across all major groups of eukaryotes, but there is considerable variance in TE diversity in different taxa. CONCLUSIONS: Differences in genome size are thought to arise primarily through accumulation of TEs, but beyond a certain point (~500 Mbp), TE diversity does not increase with genome size. Several possible explanations for these complex patterns are discussed, and recommendations to facilitate future analyses are provided.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".