Porcine reproductive and respiratory syndrome virus whole-genome sequencing efficacy with field clinical samples using a poly(A)-tail viral genome purification method
Bibliographic record
Abstract
The genomic surveillance of porcine reproductive and respiratory syndrome virus (PRRSV) is based on sequencing of the ORF5 gene of the virus, which covers only 4% of the entire viral genome. It is expected that PRRSV whole-genome sequencing (WGS) will improve PRRSV genomic data and allow better understanding of clinical discrepancies observed in the field when using ORF5 sequencing. Our main objective was to implement an efficient method for WGS of PRRSV from clinical samples. The viral genome was purified using a poly(A)-tail viral genome purification method and sequenced using Illumina technology. We tested 149 PRRSV-positive samples: 80 sera, 33 lungs, 33 pools of tissues, 2 oral fluids, and 1 processing fluid (i.e., castration liquid). Overall, WGS of 67.1% of PRRSV-positive cases was successful. The viral load, in particular for tissues, had a major impact on the PRRSV WGS success rate. Serum was the most efficient type of sample to conduct PRRSV WGS poly(A)-tail assays, with a success rate of 76.3%, and this result can be explained by improved sequencing reads dispersion matching throughout the entire viral genome. WGS was unsuccessful for all pools of tissue and lung samples with Cq values > 26.5, whereas it could still be successful with sera at Cq ≤ 34.1. Evaluation of results of highly qualified personnel confirmed that laboratory skills could affect PRRSV WGS efficiency. Oral fluid samples seem very promising and merit further investigation because, with only 2 samples of low viral load (Cq = 28.8, 32.8), PRRSV WGS was successful.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".