Diverse and pervasive subcellular distributions for both coding and long noncoding RNAs
Bibliographic record
Abstract
In a previous analysis of 2300 mRNAs via whole-mount fluorescent in situ hybridization in cellularizing Drosophila embryos, we found that 70% of the transcripts exhibited some form of subcellular localization. To see whether this prevalence is unique to early Drosophila embryos, we examined ∼8000 transcripts over the full course of embryogenesis and ∼800 transcripts in late third instar larval tissues. The numbers and varieties of new subcellular localization patterns are both striking and revealing. In the much larger cells of the third instar larva, virtually all transcripts observed showed subcellular localization in at least one tissue. We also examined the prevalence and variety of localization mechanisms for >100 long noncoding RNAs. All of these were also found to be expressed and subcellularly localized. Thus, subcellular RNA localization appears to be the norm rather than the exception for both coding and noncoding RNAs. These results, which have been annotated and made available on a recompiled database, provide a rich and unique resource for functional gene analyses, some examples of which are provided.
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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 | 0.001 |
| 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".