The organization and regulation of <scp>mRNA</scp>–protein complexes
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.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".