Target capture sequencing of SARS-CoV-2 genomes using the ONETest Coronaviruses Plus
Bibliographic record
Abstract
We introduce a target capture next-generation sequencing methodology, the ONETest Coronaviruses Plus, to sequence the SARS-CoV-2 genome and select loci of other respiratory viruses. We applied the ONETest on 70 respiratory samples (collected in Florida, USA between May and July, 2020), in which SARS-CoV-2 had been detected by a PCR assay. For 48 of the samples, we also applied the ARTIC protocol. Of the 70 ONETest libraries, 45 (64%) had a (near-)complete sequence (>29,000 bases and >90% covered by >9 reads). Of the 48 ARTIC libraries, 25 (52%) had a (near-)complete sequence. In 19 out of 25 (76%) samples in which both the ONETest and ARTIC yielded (near-)complete sequences, the lineages assigned were identical. As a target capture approach, the ONETest is less prone to loss of sequence coverage than amplicon approaches, and thus can provide complete genomic information more often to track and monitor SARS-CoV-2 variants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".