Localized Retroprocessing as a Model of Intron Loss in the Plant Mitochondrial Genome
Bibliographic record
Abstract
Loss of introns in plant mitochondrial genes is commonly explained by retroprocessing. Under this model, an mRNA is reverse transcribed and integrated back into the genome, simultaneously affecting the contents of introns and edited sites. To evaluate the extent to which retroprocessing explains intron loss, we analyzed patterns of intron content and predicted RNA editing for whole mitochondrial genomes of 30 species in the monocot order Alismatales. In this group, we found an unusually high degree of variation in the intron content, even expanding the hitherto known variation among angiosperms. Some species have lost some two-third of the cis-spliced introns. We found a strong correlation between intron content and editing frequency, and detected 27 events in which intron loss is consistent with the presence of nucleotides in an edited state, supporting retroprocessing. However, we also detected seven cases of intron loss not readily being explained by retroprocession. Our analyses are also not consistent with the entire length of a fully processed cDNA copy being integrated into the genome, but instead indicate that retroprocessing usually occurs for only part of the gene. In some cases, several rounds of retroprocessing may explain intron loss in genes completely devoid of introns. A number of taxa retroprocessing seem to be very common and a possibly ongoing process. It affects the entire mitochondrial genome.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".