Rapid, high-throughput, cost-effective whole-genome sequencing of SARS-CoV-2 using a condensed library preparation of the Illumina DNA Prep kit
Bibliographic record
Abstract
The ongoing COVID-19 pandemic necessitates cost-effective, high-throughput, and timely whole-genome sequencing (WGS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viruses for outbreak investigations, identifying variants of concern (VoC), characterizing vaccine breakthrough infections, and public health surveillance. In addition, the enormous demand for WGS on supply chains and the resulting shortages of laboratory supplies necessitated the use of low-reagent and low-consumable methods. Here, we report an optimized library preparation method (the BCCDC cutdown method) that can be used in a high-throughput scenario, where one technologist can perform 576 library preparations (6 plates of 96 samples) over the course of one 8-hour shift. The same protocol can also be used in a rapid turnaround time scenario, from primary samples (up to 96 samples) to loading on a sequencer in an 8-hour shift. This new method uses Freed et al.'s 1,200 bp primer sets (Biol Methods Protoc 5:bpaa014, 2020, https://doi.org/10.1093/biomethods/bpaa014) and a modified and condensed Illumina DNA Prep workflow (Illumina, CA, USA). Compared to the original protocol, the application of this new method using hundreds of clinical specimens demonstrated equivalent results to the full-length DNA Prep workflow at 45% of 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 demonstrate that this new method is a rapid, simple, cost-effective, and high-quality SARS-CoV-2 WGS protocol. IMPORTANCE: Sequencing has played an invaluable role in the response to the COVID-19 pandemic. Ongoing work in this area, however, demands optimization of laboratory workflow to increase sequencing capacity, improve turnaround time, and reduce cost without compromising sequence quality. This report describes an optimized DNA library preparation method for improved whole-genome sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen. The workflow advantages summarized here include significant time, cost, and consumable savings, which suggest that this new method is an efficient, scalable, and pragmatic alternative for SARS-CoV-2 whole-genome sequencing.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".