A global view of the RNA-binding and regulatory protein landscape in <i>Caenorhabditis elegans</i>
Bibliographic record
Abstract
Abstract Post-transcriptional regulation of gene expression is essential for the correct development and functioning of an organism. This regulation is coordinated by a collection of proteins that work together to determine an RNA’s post-transcriptional fate. Here, we provide a global overview of the RNA regulatory protein landscape in Caenorhabditis elegans , to provide insight into the coordination of post-transcriptional regulatory activities in the context of a multicellular organism. First, we have curated a comprehensive list of all known and putative RNA regulatory proteins encoded in the C. elegans genome, classified based on domain and functional annotations and published experimental data. Second, using protein-protein interaction data in the STRING database, we created a putative RNA regulatory protein interaction network that highlighted known RNA regulatory complexes, and leveraged this network to identify an additional 138 known and putative RNA regulators previously unannotated in C. elegans . Finally, we examined the tissue- and developmental-stage-specific expression of RNA regulators using published transcript expression data, which revealed strong expression in the gonad for a majority, as well as dozens expressed specifically in each of the major somatic C. elegans tissues. Taken together, this work will provide a valuable resource for future studies of RNA biology in C. elegans .
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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 | 0.001 | 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".