Current Perspectives on High-Throughput Sequencing (HTS) for Adventitious Virus Detection: Upstream Sample Processing and Library Preparation
Bibliographic record
Abstract
A key step for broad viral detection using high-throughput sequencing (HTS) is optimizing the sample preparation strategy for extracting viral-specific nucleic acids since viral genomes are diverse: They can be single-stranded or double-stranded RNA or DNA, and can vary from a few thousand bases to over millions of bases, which might introduce biases during nucleic acid extraction. In addition, viral particles can be enveloped or non-enveloped with variable resistance to pre-treatment, which may influence their susceptibility to extraction procedures. Since the identity of the potential adventitious agents is unknown prior to their detection, efficient sample preparation should be unbiased toward all different viral types in order to maximize the probability of detecting any potential adventitious viruses using HTS. Furthermore, the quality assessment of each step for sample processing is also a critical but challenging aspect. This paper presents our current perspectives for optimizing upstream sample processing and library preparation as part of the discussion in the Advanced Virus Detection Technologies Interest group (AVDTIG) The topics include: use of nuclease treatment to enrich for encapsidated nucleic acids, techniques for amplifying low amounts of virus nucleic acids, selection of different extraction methods, relevant controls, the use of spike recovery experiments, and quality control measures during library preparation.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".