Insights into the N<sup>6</sup>-methyladenosine mechanism and its functionality: progress and questions
Bibliographic record
Abstract
-methyladenosine (m6A) RNA methylation has become a progressively popular area of molecular research since the discovery of its potentially essential regulatory role amongst eukaryotes. m6A marks are observed in the 5'UTR, 3'UTR and coding regions of eukaryotes and its mediation has been associated with various human diseases, RNA stability and translational efficiency. To understand the implications of m6A methylation in molecular governance, its functionality and mechanism must be initially understood. m6A regulation through its readers, writers and erasers as well as an insight into the potential "cross-talk" occurring between m6A and previously well documented regulatory molecular mechanisms have been characterized. The majority of research to date has been limited to few species and has yet to explore the species- and tissue specific nature or mechanistic plasticity of m6A regulation. There is still a tremendous gap in our knowledge surrounding the mechanism and functionality of m6A RNA methylation. Here we review the formation, removal, and decoding of m6A amongst animals, yeast, and plants while noting potential "cross-talk" between various mechanisms and highlighting potential areas of future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".