Mitochondrial and plastid genome architecture: Reoccurring themes, but significant differences at the extremes
Bibliographic record
Abstract
Mitochondrial and plastid genomes show a wide array of architectures, varying immensely in size, structure, and content. Some organelle DNAs have even developed elaborate eccentricities, such as scrambled coding regions, nonstandard genetic codes, and convoluted modes of posttranscriptional modification and editing. Here, we compare and contrast the breadth of genomic complexity between mitochondrial and plastid chromosomes. Both organelle genomes have independently evolved many of the same features and taken on similar genomic embellishments, often within the same species or lineage. This trend is most likely because the nuclear-encoded proteins mediating these processes eventually leak from one organelle into the other, leading to a high likelihood of processes appearing in both compartments in parallel. However, the complexity and intensity of genomic embellishments are consistently more pronounced for mitochondria than for plastids, even when they are found in both compartments. We explore the evolutionary forces responsible for these patterns and argue that organelle DNA repair processes, mutation rates, and population genetic landscapes are all important factors leading to the observed convergence and divergence in organelle genome architecture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 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".