Targeted enrichment and viral metagenomics in the detection of livestock and wildlife viruses
Bibliographic record
Abstract
This thesis focuses on metagenomic viral detection based on high-throughput sequencing. Metagenomics sequencing results in a mixture of sequences, including viral, host and other non-target sequences. Metagenomic viral detection can lack sensitivity due to the low incidence of viral sequences in these mixtures. For this reason, enrichment methods are often employed in metagenomic viral detection. Targeted enrichment is a method based on the hybridization of probes designed to enrich the desired target from a mixture. ViroCap is a method of targeted enrichment designed for vertebrate viruses. It has previously been validated to aid in the detection of viruses from human samples. The second chapter of this thesis describes the evaluation of ViroCap in 10 wildlife and livestock hosts. Enrichment was calculated on a blinded panel of 24 animal and zoonotic viral species. The average enrichment of percent-viral-reads across the 22 viral families tested was 182-fold. Depth of coverage increased 123 times and breadth of coverage increased by 43%. In addition, 6 unexpected viruses were detected. Chapter 3 of this thesis describes the results of the use of viral metagenomics and ViroCap enrichment in a diagnostic disease investigation. ViroCap enrichment and metagenomic high-throughput sequencing revealed the presence of 4 viruses in a mass die-off of Canada and Snow geese, including avian metapneumovirus and avian adeno-associated virus. In addition, the entire genome sequence of a novel species of Gammacoronavirus is described. The first sequence information of goose adenovirus in Canada is also described. Chapter 4 describes the fecal virome of arctic and red foxes. 6 viral families were detected with the use of ViroCap targeted enrichment. A diversity of divergent circoviruses and parvoviruses are described. In addition, numerous avian influenza and canine kobuvirus subtypes were also detected in the feces of several arctic foxes. In conjunction with the description of wildlife viromes, this thesis establishes the utility of targeted enrichment in the detection of livestock and wildlife viruses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".