Genome-Wide Data-Mining of Candidate Human Splice Translational Efficiency Polymorphisms (STEPs) and an Online Database
Bibliographic record
Abstract
BACKGROUND: Variation in pre-mRNA splicing is common and in some cases caused by genetic variants in intronic splicing motifs. Recent studies into the insulin gene (INS) discovered a polymorphism in a 5' non-coding intron that influences the likelihood of intron retention in the final mRNA, extending the 5' untranslated region and maintaining protein quality. Retention was also associated with increased insulin levels, suggesting that such variants--splice translational efficiency polymorphisms (STEPs)--may relate to disease phenotypes through differential protein expression. We set out to explore the prevalence of STEPs in the human genome and validate this new category of protein quantitative trait loci (pQTL) using publicly available data. METHODOLOGY/PRINCIPAL FINDINGS: Gene transcript and variant data were collected and mined for candidate STEPs in motif regions. Sequences from transcripts containing potential STEPs were analysed for evidence of splice site recognition and an effect in expressed sequence tags (ESTs). 16 publicly released genome-wide association data sets of common diseases were searched for association to candidate polymorphisms with HapMap frequency data. Our study found 3324 candidate STEPs lying in motif sequences of 5' non-coding introns and further mining revealed 170 with transcript evidence of intron retention. 21 potential STEPs had EST evidence of intron retention or exon extension, as well as population frequency data for comparison. CONCLUSIONS/SIGNIFICANCE: Results suggest that the insulin STEP was not a unique example and that many STEPs may occur genome-wide with potentially causal effects in complex disease. An online database of STEPs is freely accessible at http://dbstep.genes.org.uk/.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".