Automated Library Construction and Analysis for High-Throughput Nanopore Sequencing of SARS-CoV-2
Bibliographic record
Abstract
BACKGROUND: To support the implementation of high-throughput pipelines suitable for SARS-CoV-2 sequencing and analysis in a clinical laboratory, we developed an automated sample preparation and analysis workflow. METHODS: We used the established ARTIC protocol with approximately 400 bp amplicons sequenced on Oxford Nanopore's MinION. Sequences were analyzed using Nextclade, assigning both a clade and quality score to each sample. RESULTS: A total of 2179 samples on twenty-five 96-well plates were sequenced. Plates of purified RNA were processed within 12 h, sequencing required up to 24 h, and analysis of each pooled plate required 1 h. The use of samples with known threshold cycle (Ct) values enabled normalization, acted as a quality control check, and revealed a strong correlation between sample Ct values and successful analysis, with 85% of samples with Ct < 30 achieving a "good" Nextclade score. Less abundant samples responded to enrichment with the fraction of Ct > 30 samples achieving a "good" classification rising by 60% after addition of a post-ARTIC PCR normalization. Serial dilutions of 3 variant of concern samples, diluted from approximately Ct = 16 to approximately Ct = 50, demonstrated successful sequencing to Ct = 37. The sample set contained a median of 24 mutations per sample and a total of 1281 unique mutations with reduced sequence read coverage noted in some regions of some samples. A total of 10 separate strains were observed in the sample set, including 3 variants of concern prevalent in British Columbia in the spring of 2021. CONCLUSIONS: We demonstrated a robust automated sequencing pipeline that takes advantage of input Ct values to improve reliability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".