Frame disruptions in human mRNA transcripts, and their relationship with splicing and protein structures
Bibliographic record
Abstract
BACKGROUND: Efforts to gather genomic evidence for the processes of gene evolution are ongoing, and are closely coupled to improved gene annotation methods. Such annotation is complicated by the occurrence of disrupted mRNAs (dmRNAs), harbouring frameshifts and premature stop codons, which can be considered indicators of decay into pseudogenes. RESULTS: We have derived a procedure to annotate dmRNAs, and have applied it to human data. Subsequences are generated from parsing at key frame-disruption positions and are required to align significantly within any original protein homology. We find 419 high-quality human dmRNAs (3% of total). Significant dmRNA subpopulations include: zinc-finger-containing transcription factors with long disrupted exons, and antisense homologies to distal genes. We analysed the distribution of initial frame disruptions in dmRNAs with respect to positions of: (i) protein domains, (ii) alternatively-spliced exons, and (iii) regions susceptible to nonsense-mediated decay (NMD). We find significant avoidance of protein-domain disruption (indicating a selection pressure for this), and highly significant overrepresentation of disruptions in alternatively-spliced exons, and 'non-NMD' regions. We do not find any evidence for evolution of novelty in protein structures through frameshifting. CONCLUSION: Our results indicate largely negative selection pressures related to frame disruption during gene evolution.
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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 | 0.001 |
| 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) | 0.002 | 0.001 |
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".