Interdisciplinary Collaboration to Develop a Custom Genomic Analysis Pipeline for the Clinical Laboratory: Hepatitis B Virus and Cytomegalovirus Antiviral Resistance Genotyping
Bibliographic record
Abstract
BACKGROUND: Next-generation sequencing for hepatitis B virus (HBV) and cytomegalovirus (CMV) antiviral drug resistance (AVDR) testing improves the sensitivity of variant detection, but availability of bioinformatics and analytical pipelines are key barriers to implementation. METHODS: Plasma was extracted on MagNA Pure 24 (Roche Diagnostics) and next-generation sequencing performed on GridION (Oxford Nanopore Technologies) with R.10.4.1 flowcells. An in-house bioinformatics pipeline was developed using Nextflow and deployed on Microsoft Azure to process FASTQ files and automate reporting of HBV genotype and AVDR, as well as CMV AVDR (UL97/54). RESULTS: A total of 71 samples for HBV genotyping and AVDR testing and 56 samples for CMV AVDR testing were analyzed and compared to reference pipelines (DeepChek® HBV and CMV). All HBV genotypes and resistant mutations were concordant. For CMV, 74 mutations were identified in the UL97/54 region by both pipelines. However, our in-house developed method identified an additional UL97 drug resistant mutation (del598-603) in one sample. CONCLUSIONS: A custom bioinformatics pipeline was developed for HBV and CMV genotyping and AVDR sequencing, which could be adapted to other targets to enable our clinical laboratory to expand clinical testing using next-generation sequencing.
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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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".