Regulatory variations in the era of next-generation sequencing: Implications for clinical molecular diagnostics
Bibliographic record
Abstract
With the successful identification of many protein-coding genes, the focus has now shifted toward deciphering functions of non-protein-coding regions that direct spatiotemporal and quantitative aspects of protein expression. Recent advances in our understanding of the regulatory architecture of the human genome coincide with growing evidence that changes in regulatory sequences are associated with human disease. Several recent reviews have highlighted disease-causing potential of aberrations in transcriptional and splicing regulatory elements as well as non-protein-coding RNA. Although changes in regulatory sequences generally produce milder biological effects than their protein-coding counterparts, many act as independent risk factors for common complex disorders or as genetic modifiers for "primary" disease-causing loci. Here, we review bioinformatics and experimental approaches that are used to identify regulatory sequences and assess pathogenicity of regulatory changes. We describe the current state of knowledge on disease-causing changes in regulatory sequences, challenge protein-centric views, and discuss complexities and solutions pertaining to the interpretation of regulatory changes in the next-generation sequencing era.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".