Rapid, High-Throughput, Cost Effective Whole Genome Sequencing of SARS-CoV-2 Using a Condensed One Hour Library Preparation of the Illumina DNA Prep Kit
Bibliographic record
Abstract
ABSTRACT The ongoing COVID-19 pandemic necessitates cost-effective, high-throughput, and timely genomic sequencing of SARS-CoV-2 viruses for outbreak investigations, identifying variants of concern (VoC), characterizing vaccine breakthrough infections, and public health surveillance. Additionally, the enormous demand of genomic sequencing on supply chains and the resulting shortages of laboratory supplies necessitate the use of low-reagent and low-consumable methods. Here, we report an optimized library preparation method where the same protocol can be used in a STAT scenario, from sample to sequencer in as little as eight hours, and a high-throughput scenario, where one technologist can perform 576 library preparations over the course of one 8-hour shift. This new method uses Freed et al.’s 1200 bp primer sets (Biol Methods Protoc 5:bpaa014, 2020, https://doi.org/10.1093/biomethods/bpaa014 ) and a modified and truncated Illumina DNA Prep workflow (Illumina, CA, USA). Compared to the original, application of this new method in hundreds of clinical specimens demonstrated equivalent results to the full-length DNA Prep workflow at 45% the cost, 15% of consumables required (such as pipet tips), 25% of manual hands-on time, and 15% of on-instrument time if performing on a liquid handler, with no compromise in sequence quality. Results suggest that this new method is a rapid, simple, cost-effective, and high-quality SARS-CoV-2 whole genome sequencing protocol.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".