Exome sequencing in genetic disease: recent advances and considerations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it