Exome sequencing: Dual role as a discovery and diagnostic tool
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
Recent developments in high-throughput sequence capture methods and next-generation sequencing technologies have now made exome sequencing a viable approach to elucidate the genetic basis of Mendelian disorders with hitherto unknown etiology. In addition, exome sequencing is increasingly being employed as a diagnostic tool for specific genetic diseases, particularly in the context of those disorders characterized by significant genetic and phenotypic heterogeneity, for example, Charcot-Marie-Tooth disease and congenital disorders of glycosylation. Such disorders are challenging to interrogate with conventional polymerase chain reaction-Sanger sequencing methods, because of the inherent difficulty in prioritizing candidate genes for diagnostic testing. Here, we explore the value of exome sequencing as a diagnostic tool and discuss whether exome sequencing can come to serve a dual role in diagnosis and discovery. We summarize the current status of exome sequencing, the technical challenges facing it, and its adaptation to diagnostics, and make recommendations for the use of exome sequencing as a routine diagnostic tool. Finally, we discuss pertinent ethical concerns, such as the use of exome sequencing data, originally generated in a diagnostic context, in research investigations.
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) | 0.001 | 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 | 0.000 |
| 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