Application of a Sanger-Based External Quality Assurance Strategy for the Transition of HIV-1 Drug Resistance Assays to Next Generation Sequencing
Bibliographic record
Abstract
The National Institute of Allergy and Infectious Diseases (NIAID) Virology Quality Assurance (VQA) established a robust proficiency testing program for Sanger sequencing (SS)-based HIV-1 drug resistance (HIVDR) testing in 2001. While many of the lessons learned during the development of such programs may also apply to next generation sequencing (NGS)-based HIVDR assays, challenges remain for the ongoing evaluation of NGS-based testing. These challenges include a proper assessment of assay accuracy and the reproducibility of low abundance variant detection, intra- and inter-assay performance comparisons among laboratories using lab-defined tests, and different data analysis pipelines designed for NGS. In collaboration with the World Health Organization (WHO) Global HIVDR Laboratory Network and the Public Health Agency of Canada, the Rush VQA program distributed archived proficiency testing panels to ten laboratories to evaluate internally developed NGS assays. Consensus FASTA files were submitted using 5%, 10%, and 20% variant detection thresholds, and scored based on the same criteria used for SS. This small study showed that the SS External Quality Assurance (EQA) approach can be used as a transitional strategy for using NGS to generate SS-like data and for ongoing performance while using NGS data from the same quality control materials to further evaluate NGS assay performance.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".