Complete sequence verification of plasmid DNA using the Oxford Nanopore Technologies’ MinION device
Bibliographic record
Abstract
BACKGROUND: Sequence verification is essential for plasmids used as critical reagents or therapeutic products. Typically, high-quality plasmid sequence is achieved through capillary-based Sanger sequencing, requiring customized sets of primers for each plasmid. This process can become expensive, particularly for applications where the validated sequence needs to be produced within a regulated and quality-controlled environment for downstream clinical research applications. RESULTS: Here, we describe a cost-effective and accurate plasmid sequencing and consensus generation procedure using the Oxford Nanopore Technologies' MinION device as an alternative to capillary-based plasmid sequencing options. This procedure can verify the identity of a pure population of plasmid, either confirming it matches the known and expected sequence, or identifying mutations present in the plasmid if any exist. We use a full MinION flow cell per plasmid, maximizing available data and allowing for stringent quality filters. Pseudopairing reads for consensus base calling reduces read error rates from 5.3 to 0.53%, and our pileup consensus approach provides per-base counts and confidence scores, allowing for interpretation of the certainty of the resulting consensus sequences. For pure plasmid samples, we demonstrate 100% accuracy in the resulting consensus sequence, and the sensitivity to detect small mutations such as insertions, deletions, and single nucleotide variants. In test cases where the sequenced pool of plasmids contains subclonal templates, detection sensitivity is similar to that of traditional capillary sequencing. CONCLUSIONS: Our pipeline can provide significant cost savings compared to outsourcing clinical-grade sequencing of plasmids, making generation of high-quality plasmid sequence for clinical sequence verification more accessible. While other long-read-based methods offer higher-throughput and less cost, our pipeline produces complete and accurate sequence verification for cases where absolute sequence accuracy is required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".