A framework for real-time monitoring, analysis and adaptive sampling of viral amplicon nanopore sequencing
Bibliographic record
Abstract
barcoding onto a single flow cell, resulting in challenges with maximising and balancing coverage for each sample. To address this, we developed a real-time analysis pipeline to maximise flow cell performance and optimise sequencing time and costs for any amplicon based sequencing. We extended our nanopore analysis platform MinoTour to incorporate ARTIC network bioinformatics analysis pipelines. MinoTour predicts which samples will reach sufficient coverage for downstream analysis and runs the ARTIC networks Medaka pipeline once sufficient coverage has been reached. We show that stopping a viral sequencing run earlier, at the point that sufficient data has become available, has no negative effect on subsequent down-stream analysis. A separate tool, SwordFish, is used to automate adaptive sampling on Nanopore sequencers during the sequencing run. This enables normalisation of coverage both within (amplicons) and between samples (barcodes) on barcoded sequencing runs. We show that this process enriches under-represented samples and amplicons in a library as well as reducing the time taken to obtain complete genomes without affecting the consensus sequence.
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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 | 0.004 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".