Trypanosome <scp>RNA</scp> editing: the complexity of getting U in and taking U out
Bibliographic record
Abstract
RNA editing, which adds sequence information to RNAs post-transcriptionally, is a widespread phenomenon throughout eukaryotes. The most complex form of this process is the uridine (U) insertion/deletion editing that occurs in the mitochondria of kinetoplastid protists. RNA editing in these flagellates is specified by trans-acting guide RNAs and entails the insertion of hundreds and deletion of dozens of U residues from mitochondrial RNAs to produce mature, translatable mRNAs. An emerging model indicates that the machinery required for trypanosome RNA editing is much more complicated than previously appreciated. A family of RNA editing core complexes (RECCs), which contain the required enzymes and several structural proteins, catalyze cycles of U insertion and deletion. A second, dynamic multiprotein complex, the Mitochondrial RNA Binding 1 (MRB1) complex, has recently come to light as another essential component of the trypanosome RNA editing machinery. MRB1 likely serves as the platform for kinetoplastid RNA editing, and plays critical roles in RNA utilization and editing processivity. MRB1 also appears to act as a hub for coordination of RNA editing with additional mitochondrial RNA processing events. This review highlights the current knowledge regarding the complex molecular machinery involved in trypanosome RNA editing. WIREs RNA 2016, 7:33-51. doi: 10.1002/wrna.1313 For further resources related to this article, please visit the WIREs website.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".