Exome sequencing in genetic disease: recent advances and considerations
Bibliographic record
Abstract
Over the past decade, exome sequencing (ES) has allowed significant advancements to the field of disease research. By targeting the protein-coding regions of the genome, ES combines the depth of knowledge on protein-altering variants with high-throughput data generation and ease of analysis. New discoveries continue to be made using ES, and medical science has benefitted both theoretically and clinically from its continued use. In this review, we describe recent advances and successes of ES in disease research. Through selected examples of recent publications, we explore how ES continues to be a valuable tool to find variants that might explain disease etiology or provide insight into the biology underlying the disease. We then discuss shortcomings of ES in terms of variant discoveries made by other sequencing technologies that would be missed because of the scope and techniques of ES. We conclude with a brief outlook on the future of ES, suggesting that although newer and more thorough sequencing methods will soon supplant ES, its results will continue to be useful for disease research.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".