A prometastatic splicing program regulated by SNRPA1 interactions with structured RNA elements
Bibliographic record
Abstract
Characterizing a cancer spliceosome Cells undergo many genomic changes as they progress toward metastatic cancer. One aspect of this change is to RNA expression and splicing isoforms, but how these differences affect tumor progression is not well characterized. Fish et al. developed a computational framework called pyTEISER that identifies structural cis-regulatory elements that control diverse types of RNA regulation. Applying pyTEISER to models of breast cancer metastasis, they discovered an RNA short-stem-loop element that forms a “structural splicing enhancer” that acts in cis to regulate alternative splicing of RNA transcripts. One of these interactions encompasses the RNA-binding protein SNRPA1 and results in alternative exon inclusion that affects metastatic capacity in xenograft models. Thus, RNA element binding may play a role in splicing regulation and is potentially an important component of the cis-splicing code. Science , this issue p. eabc7531
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".