Skip and bin: pervasive alternative splicing triggers degradation by nuclear RNA surveillance in fission yeast
Bibliographic record
Abstract
Abstract Exon-skipping is considered a principal mechanism by which eukaryotic cells expand their transcriptome and proteome repertoires, creating different slice varaiants with distinct cellular functions. Here we analyze RNA-seq data from 116 transcriptomes in fission yeast ( Schizosaccharomyces pombe ), covering multiple physiological conditions as well as transcriptional and RNA processing mutants. We applied brute-force algorithms to detect all possible exon-skipping events, which were ubiquitous but rare compared to canonical splicing events. Exon-skipping events increased in cells deficient for the nuclear exosome or the 5’-3’ exonuclease Dhp1, and also at late stages of meiotic differentiation when nuclear-exosome transcripts were down-regulated. The pervasive exon-skipping transcripts were stochastic, did not increase in specific physiological conditions, and were mostly present at below 1 copy per cell, even in the absence of nuclear RNA surveillance and late during meiosis. These exon-skipping transcripts are therefore unlikely to be functional and may reflect splicing errors that are actively removed by nuclear RNA surveillance. The average splicing error-rate was ∼0.24% in wild-type and ∼1.75% in nuclear exonuclease mutants. Using an exhaustive search algorithm, we also uncovered thousands of previously unknown splice sites, indicating pervasive splicing, yet most of these novel splicing events were rare and targeted for nuclear degradation. Analysis of human transcriptomes revealed similar, albeit much weaker trends for pervasive exon-skipping transcripts, some of which being degraded by the nuclear exosome. This study highlights widespread, but low frequency alternative splicing which is targeted by nuclear RNA surveillance.
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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 | 0.000 | 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) | 0.000 | 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".