Leveraging long-read sequencing technologies for pharmacogenomic testing: applications, analytical strategies, challenges, and future perspectives
Bibliographic record
Abstract
Long-read sequencing (LRS) was introduced as the third generation of next-generation sequencing technologies with a high accuracy rate in genomic variant identification for some of its platforms. Due to the structural complexity of many pharmacogenes, the presence of rare variants, and the limitations of genotyping and short-read sequencing approaches in detecting pharmacovariants, LRS methods are likely to become increasingly utilized in the near future. In this review, we aim to provide a comprehensive discussion of current and future applications of long-read genotyping methods by introducing the opportunities and advantages as well as the challenges and disadvantages of state-of-the-art LRS platforms for the implementation of pharmacogenomic tests in clinical and research settings. New approaches to data processing, as well as the challenges and pitfalls of performing such tests in daily practice, will be explored in detail. We provide references to resources for those who are interested or intend to employ LRS in pharmacogenomics screening, both in clinical and research settings.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".