The organization and regulation of <scp>mRNA</scp>–protein complexes
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
In a eukaryotic cell, each messenger RNA (mRNA) is bound to a variety of proteins to form an mRNA-protein complex (mRNP). Together, these proteins impact nearly every step in the life cycle of an mRNA and are critical for the proper control of gene expression. In the cytoplasm, for instance, mRNPs affect mRNA translatability and stability and provide regulation of specific transcripts as well as global, transcriptome-wide control. mRNPs are complex, diverse, and dynamic, and so they have been a challenge to understand. But the advent of high-throughput sequencing technology has heralded a new era in the study of mRNPs. Here, I will discuss general principles of cytoplasmic mRNP organization and regulation. Using microRNA-mediated repression as a case study, I will focus on common themes in mRNPs and highlight the interplay between mRNP composition and posttranscriptional regulation. mRNPs are an important control point in regulating gene expression, and while the study of these fascinating complexes presents remaining challenges, recent advances provide a critical lens for deciphering gene regulation. WIREs RNA 2017, 8:e1369. doi: 10.1002/wrna.1369 For further resources related to this article, please visit the WIREs website.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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