Pervasive Transcription of Mitochondrial, Plastid, and Nucleomorph Genomes across Diverse Plastid-Bearing Species
Bibliographic record
Abstract
Organelle genomes exhibit remarkable diversity in content, structure, and size, and in their modes of gene expression, which are governed by both organelle- and nuclear-encoded machinery. Next generation sequencing (NGS) has generated unprecedented amounts of genomic and transcriptomic data, which can be used to investigate organelle genome transcription. However, most of the available eukaryotic RNA-sequencing (RNA-seq) data are used to study nuclear transcription only, even though large numbers of organelle-derived reads can typically be mined from these experiments. Here, we use publicly available RNA-seq data to assess organelle genome transcription in 59 diverse plastid-bearing species. Our RNA mapping analyses unraveled pervasive (full or near-full) transcription of mitochondrial, plastid, and nucleomorph genomes. In all cases, 85% or more of the organelle genome was recovered from the RNA data, including noncoding (intergenic and intronic) regions. These results reinforce the idea that organelles transcribe all or nearly all of their genomic material and are dependent on post-transcriptional processing of polycistronic transcripts. We explore the possibility that transcribed intergenic regions are producing functional noncoding RNAs, and that organelle genome noncoding content might provide raw material for generating regulatory RNAs.
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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 | 0.000 |
| 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".