Implementation of Next-Generation Sequencing for Hepatitis B Virus Resistance Testing and Genotyping in a Clinical Microbiology Laboratory
Bibliographic record
Abstract
Sanger sequencing or DNA hybridization have been the primary modalities for hepatitis B (HBV) resistance testing and genotyping; however, there are limitations, such as low sensitivity and the inability to detect novel mutations. Next-generation sequencing (NGS) for HBV can overcome these limitations, but there is limited guidance for clinical microbiology laboratories to validate this novel technology. In this study, we describe an approach to implementing deep pyrosequencing for HBV resistance testing and genotyping in a clinical virology laboratory. A nested PCR targeting the pol region of HBV (codons 143 to 281) was developed, and the PCR product was sequenced by the 454 Junior (Roche). Interpretation was performed by ABL TherapyEdge based on European Association for the Study of the Liver (EASL) guidelines. Previously characterized HBV samples by INNO-LiPA (LiPA) were compared to NGS with discordant results arbitrated by Sanger sequencing. Genotyping of 105 distinct samples revealed a concordance of 95.2% (100/105), with Sanger sequencing confirming the NGS result. Resistance testing by NGS was concordant with LiPA in 85% (68/80) of previously characterized samples. Additional mutations were found in 8 samples, which related to the identification of low-level mutant subpopulations present at <10% (6/8). To balance the costs of testing for the validation study, reproducibility of the NGS was investigated through an analysis of sequence variants at loci not associated with resistance in a single patient sample. Our validation approach attempts to balance costs with efficient data acquisition.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".