Ciphers and Executioners: How 3′-Untranslated Regions Determine the Fate of Messenger RNAs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The sequences and structures of 3’-untranslated regions (3’UTRs) of messenger RNAs govern their stability, localization, and expression. 3’UTRs regulatory elements are recognized by a diverse variety of trans-acting factors that include microRNAs (miRNAs) and their associated machinery, and RNA-binding proteins (RBPs). In turn, these factors instigate common mechanistic strategies to execute the regulatory programs encoded by 3’UTRs. Here, we review classes of factor that recognize 3’UTR regulatory elements and the effector machineries they guide towards mRNAs to dictate their expression and fate. We outline illustrative examples of competitive, cooperative, and coordinated interplay such as mRNA localization and localized translation. We further review the recent advances in mRNP granules and phase transition, and their significance for the functions of 3’UTRs. Finally, we highlight some of the most recent strategies aimed at deciphering the complexity of the regulatory codes of 3’UTRs, and identify some of the important remaining challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it