Virus detection by short read high throughput sequencing in a high virus low cellular background
Bibliographic record
Abstract
Abstract The safety of all biological products includes demonstrating the absence of adventitious viruses by testing various types of samples at different stages of the manufacturing process. Seven laboratories evaluated short-read high-throughput sequencing (HTS) for sensitivity and breadth of adventitious virus detection using viruses with distinct physicochemical and genome properties. These five viruses are currently designated as CBER NGS Virus Reagents and include: Epstein-Barr virus (EBV; or human herpes virus 4), feline leukemia virus (FeLV), respiratory syncytial virus (RSV), mammalian orthoreovirus type 1 (Reo1), and porcine circovirus type 1 (PCV1). To evaluate adventitious virus detection in a biological material with a high production virus titer and low cellular background, the 5 viruses were mixed and different copies of the viral genomes spiked into 1 – 5 × 109 genome copies per mL (GC/mL) of purified adenovirus 5. Independent protocols were used by each laboratory for the entire HTS workflow. All laboratories detected 104 GC/mL of the five viruses by both targeted and non-targeted bioinformatic analyses. Additionally, the limit of detection of squirrel monkey retrovirus and porcine endogenous retrovirus, which pre-existed in EBV and PCV1 virus stocks, respectively, was evaluated. The five laboratories that tested 103 GC/mL, detected all 5 viruses with the targeted analysis, and Reo1 and EBV with the non-targeted analysis. It was noted that some laboratories achieved a better sensitivity for detection of the five viruses ( ≤102 GC/mL). This study presents an approach for HTS validation for viral safety testing of vaccines and other biologics using a panel of reference viruses. The results highlight that optimization of steps in the HTS workflow can improve the limit of detection for adventitious viruses.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".