The low information content of <i>Neurospora</i> splicing signals: implications for RNA splicing and intron origin
Bibliographic record
Abstract
When we expressed a small (0.9 kb) nonprotein-coding transcript derived from the mitochondrial VS plasmid in the nucleus of Neurospora we found that it was efficiently spliced at one or more of eight 5' splice sites and ten 3' splice sites, which are present apparently by chance in the sequence. Further experimental and bioinformatic analyses of other mitochondrial plasmids, random sequences, and natural nuclear genes in Neurospora and other fungi indicate that fungal spliceosomes recognize a wide range of 5' splice site and branchpoint sequences and predict introns to be present at high frequency in random sequence. In contrast, analysis of intronless fungal nuclear genes indicates that branchpoint, 5' splice site and 3' splice site consensus sequences are underrepresented compared with random sequences. This underrepresentation of splicing signals is sufficient to deplete the nuclear genome of splice sites at locations that do not comprise biologically relevant introns. Thus, the splicing machinery can recognize a wide range of splicing signal sequences, but splicing still occurs with great accuracy, not because the splicing machinery distinguishes correct from incorrect introns, but because incorrect introns are substantially depleted from the genome.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".