Evaluation of a next generation sequencing assay for Hepatitis B antiviral drug resistance on the oxford nanopore system
Bibliographic record
Abstract
• Next-generation sequencing on the Miseq and Oxford nanopore system were comparable for the detection of HBV genotype and antiviral resistance (AVR) mutations. • Oxford nanopore System, with updated R10.4.1 flowcells, provides accurate AVR mutation identification with <1 % sequence error rates. • Oxford nanopore system demonstrated improved testing turn-around time for clinical sequencing results. Next-generation sequencing (NGS) for Hepatitis B virus (HBV) antiviral resistance (AVR) testing is a highly sensitive diagnostic method, able to detect low-level mutant subpopulations. Our clinical virology laboratory previously transitioned from DNA hybridization (INNO-LiPA) to NGS, initially with the GS Junior System and subsequently the MiSeq. The Oxford Nanopore Technology (ONT) sequencing system was evaluated for HBV resistance testing, with regards to sequencing accuracy and turn-around time. We performed amplicon sequencing of the HBV polymerase gene from patient plasma and external quality assessment (EQA) samples on the MiSeq Reagent Nano Kit v2 and GridION ONT with R10.4.1 flowcells. Mutational analysis and genotyping were performed by DeepChek®Assay-HBV (version 2.0). A total of 49 patient samples and 15 EQA samples were tested on both the MiSeq and ONT. There was high agreement for both patient and EQA samples between the MiSeq and ONT systems, with regards to total drug resistance mutations detected and total patient sample agreement, 68/70 (97 %) and 47/49 (96 %), respectively. The ONT NGS platform provided accurate HBV AVR results, with improved turn-around times. Sequencing error rates at AVR codons were below 1 %.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".