Deep Conservation of MicroRNA-target Relationships and 3'UTR Motifs in Vertebrates, Flies, and Nematodes
Bibliographic record
Abstract
microRNAs (miRNAs) are a class of small noncoding RNAs that posttranscriptionally regulate a large fraction of genes in animal genomes. We have previously published computational miRNA target predictions in five vertebrates, six flies, and three nematodes. Here, we report a comprehensive study of the "deep" conservation of miRNA targets and conserved 3'UTR (untranslated region) motifs in general across vertebrates, flies, and nematodes. Our data indicate that although many miRNA genes and 3'UTR motifs are well-conserved, miRNA-target relationships have diverged more rapidly, and we explicitly assign each gained or lost miRNA-target relationship to one of the three clades. However, we also identify a small but significant number of deeply conserved miRNA targets and show that these are enriched for essential processes related to development. Finally, we provide lists of 3'UTR motifs that are significantly conserved, and thus likely functional, classified by their distribution in the three clades. We find hundreds of such motifs specific to each clade, dozens specific to each pair of clades, and ten shared by vertebrates, flies, and nematodes. These findings suggest that posttranscriptional control has undergone extensive rewiring during metazoan evolution and that many deeply conserved miRNA-target relationships may be vital subunits of metazoan gene regulatory networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".