Global analysis of mRNA localization reveals a prominent role in the organization of cellular architecture and function
Bibliographic record
Abstract
The localization of mRNA molecules is an important regulatory mechanism for targeting proteins to specific cellular compartments, although the overall prevalence and variety of transcript localization events remains unknown. To characterize subcellular mRNA localization dynamics during early Drosophila embryogenesis, we conducted a high‐throughput Fluorescent In Situ Hybridization (FISH) screen of over 4,000 distinct mRNAs, and found that the majority of expressed mRNAs (71%) are subcellularly localized. Many novel varieties of subcellular localization patterns were identified, implicating localized mRNAs in the assembly and regulation of diverse cellular modules and processes. Analysis of the localization data, which has been organized within a publicly available database ( http://fly‐fish.ccbr.utoronto.ca ), reveals that transcripts with similar localization dynamics are enriched for specific gene functions and putative regulatory elements. This work establishes mRNA localization as a widespread gene regulatory mechanism and underscores the predictive value of transcript localization phenotypes in assigning gene functions. We have begun dissecting the localization mechanisms and biological functions of various classes of mRNAs and the results of these ongoing studies will be presented. Funding provided by the Canadian Institutes of Health 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".