The many functions of mRNA localization during normal development and disease: from pillar to post
Bibliographic record
Abstract
Abstract The regulated intracellular trafficking and localized translation of mRNA molecules represents an important and prevalent mechanism of gene regulation. This process plays a key role in modulating asymmetric protein distribution linked to a wide variety of biological processes in different organisms and cell types. In this review, we begin by discussing the diverse biological functions, advantages, and mechanisms of mRNA localization that have been characterized to date. We then review recent technological innovations in RNA imaging and functional genomics methods that will undoubtedly provide powerful new strategies for the elucidation of mRNA trafficking pathways. Finally, we discuss several examples linking human disease pathogenesis to defects in transcript localization, which further underlines the critical importance of this gene regulatory mechanism. WIREs Dev Biol 2013, 2:781–796. doi: 10.1002/wdev.113 This article is categorized under: Gene Expression and Transcriptional Hierarchies > Regulatory Mechanisms Gene Expression and Transcriptional Hierarchies > Regulatory RNA Technologies > Analysis of Cell, Tissue, and Animal Phenotypes Technologies > Analysis of the Transcriptome
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".