Characterization of the Genomic Diversity of Norovirus in Linked Patients Using a Metagenomic Deep Sequencing Approach
Bibliographic record
Abstract
Norovirus (NoV) is the leading cause of gastroenteritis worldwide. A robust cell culture system does not exist for NoV and therefore detailed characterization of outbreak and sporadic strains relies on molecular techniques. In this study, we employed a metagenomic approach that uses non-specific amplification followed by next-generation sequencing to whole genome sequence NoV genomes directly from clinical samples obtained from 8 linked patients. Enough sequencing depth was obtained for each sample to use a de novo assembly of near-complete genome sequences. The resultant consensus sequences were then used to identify inter-host nucleotide variations that occur after direct transmission, analyze amino acid variations in the major capsid protein, and provide evidence of recombination events. The analysis of intra-host quasispecies diversity was possible due to high coverage-depth. We also observed a linear relationship between NoV viral load in the clinical sample and the number of sequence reads that could be attributed to NoV. The method demonstrated here has the potential for future use in whole genome sequence analyses of other RNA viruses isolated from clinical, environmental, and food specimens.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".