Complete Hepatitis B Virus Sequencing using an ONT-Based Next-Generation Sequencing Protocol v1
Bibliographic record
Abstract
This protocol outlines the process of generating complete Hepatitis B virus (HBV) primers from DNA extracts for next-generation sequencing using Oxford Nanopore Technology (ONT). Specifically, we have designed pan-genotypic tiling primers to cover the entire HBV genome for sequencing. The hands-on time required for a batch of 48 samples is minimal, approximately 1 hour and 30 minutes. This protocol is straightforward and can be easily adapted in settings where the ONT protocol for SARS-CoV-2 has been implemented. We provide detailed instructions for PCR amplification using HBV-specific tiling primers, sample pooling, library construction using the Rapid Barcoding Kit (SQK-RBK110.96), quantification using Qubit, and subsequent sequencing on the GridION platform. The Rapid barcoding protocol for up-to 96 samples "PCR tiling of SARS-CoV-2 virus with rapid barcoding (SQK-RBK110.96)" https://community.nanoporetech.com/protocols/pcr-tiling-of-sars-cov-2-virus-with-rapid-barcoding-sqk-rbk110/v/PCTR_9125_v110_revA_24Mar2021. This protocol describes a modified version of the 1200bp amplicon "midnight" primer set for nCoV-2019 (SARS-CoV-2) amplification, utilizing the Nanopore Rapid kit for MinION. The modification was developed by Nikki Freed. https://dx.doi.org/10.17504/protocols.io.bwyppfvn and the original publication is found here : https://doi.org/10.1093/biomethods/bpaa014. Primers were all designed using Primal Scheme: http://primal.zibraproject.org/, described here https://www.nature.com/articles/nprot.2017.066. Primer sequences are here (and listed directly in the protocol) :
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.024 |
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".