A SARS-CoV-2 Surveillance Sequencing Protocol Optimized for Oxford Nanopore PromethION v1
Bibliographic record
Abstract
To identify and monitor SARS-CoV-2 variant evolution, UNC Charlotte has introduced a surveillance sequencing program for the campus and surrounding county area. We have generated a step by step protocol which has been optimized from the ”High Throughput Nanopore Sequencing of SARS-CoV-2 Viral Genomes from Patient Samples” (Pater et al 2021, https://doi.org/10.1101/2021.02.09.430478) protocol for preparing SARS-CoV 2 viral genome libraries for next generation sequencing using the Oxford Nanopore PromethION instrument. The protocol is designed to work in a 96-well format throughout. It is intended to produce sufficient sequence for genome assembly to meet standards required by GISAID and NCBI for SARS-CoV-2 sequence submission. The protocol has been modified to achieve maximum yield and includes modifications to address isolate sequencing failures arising from low viral titer in some clinical samples (Cq values below 30). The protocol provides detailed guidelines and steps for working with high Cq value clinical samples, as well as a protocol variation for working in half-reaction volumes throughout to reduce sequencing costs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.026 |
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".