Mini-XT, a miniaturized tagmentation-based protocol for efficient sequencing of SARS-CoV-2
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has highlighted the importance of whole genome sequencing (WGS) of SARS-CoV-2 to inform public health policy. By enabling definition of lineages it facilitates tracking of the global spread of the virus. The evolution of new variants can be monitored and knowledge of specific mutations provides insights into the mechanisms through which the virus increases transmissibility or evades immunity. To date almost 1 million SARS-CoV-2 genomes have been sequenced by members of the COVID-19 Genomics UK (COG-UK) Consortium. To achieve similar feats in a more cost-effective and sustainable manner in future, improved high throughput virus sequencing protocols are required. We have therefore developed a miniaturized library preparation protocol with drastically reduced consumable use and costs. RESULTS: We present the 'Mini-XT' miniaturized tagmentation-based library preparation protocol available on protocols.io ( https://doi.org/10.17504/protocols.io.bvntn5en ). SARS-CoV-2 RNA was amplified using the ARTIC nCov-2019 multiplex RT-PCR protocol and purified using a conventional liquid handling system. Acoustic liquid transfer (Echo 525) was employed to reduce reaction volumes and the number of tips required for a Nextera XT library preparation. Sequencing was performed on an Illumina MiSeq. The final version of Mini-XT has been used to sequence 4384 SARS-CoV-2 samples from N. Ireland with a COG-UK QC pass rate of 97.4%. Sequencing quality was comparable and lineage calling consistent for replicate samples processed with full volume Nextera DNA Flex (333 samples) or using nanopore technology (20 samples). SNP calling between Mini-XT and these technologies was consistent and sequences from replicate samples paired together in maximum likelihood phylogenetic trees. CONCLUSIONS: The Mini-XT protocol maintains sequence quality while reducing library preparation reagent volumes eightfold and halving overall tip usage from sample to sequence to provide concomitant cost savings relative to standard protocols. This will enable more efficient high-throughput sequencing of SARS-CoV-2 isolates and future pathogen WGS.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".