A Development of Rapid Whole-Genome Sequencing of Seoul orthohantavirus Using a Portable One-Step Amplicon-Based High Accuracy Nanopore System
Bibliographic record
Abstract
Whole-genome sequencing provides a robust platform for investigating the epidemiology and transmission of emerging viruses. Oxford Nanopore Technologies allows for real-time viral sequencing on a local laptop system for point-of-care testing. Seoul orthohantavirus (Seoul virus, SEOV), harbored by Rattus norvegicus and R. rattus, causes mild hemorrhagic fever with renal syndrome and poses an important threat to public health worldwide. We evaluated the deployable MinION system to obtain high-fidelity entire-length sequences of SEOV for the genome identification of accurate infectious sources and their genetic diversity. One-step amplicon-based nanopore sequencing was performed from SEOV 80–39 specimens with different viral copy numbers and SEOV-positive wild rats. The KU-ONT-SEOV-consensus module was developed to analyze SEOV genomic sequences generated from the nanopore system. Using amplicon-based nanopore sequencing and the KU-ONT-consensus pipeline, we demonstrated novel molecular diagnostics for acquiring full-length SEOV genome sequences, with sufficient read depth in less than 6 h. The consensus sequence accuracy of the SEOV small, medium, and large genomes showed 99.75–100% (for SEOV 80–39 isolate) and 99.62–99.89% (for SEOV-positive rats) identities. This study provides useful insights into on-site diagnostics based on nanopore technology and the genome epidemiology of orthohantaviruses for a quicker response to hantaviral outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".