Development and evaluation of a next-generation sequencing methodology for measles virus using Oxford Nanopore Technology
Bibliographic record
Abstract
We report the development of a bench protocol and evaluation of bioinformatics pipelines for the whole genome sequence (WGS) of measles virus (MeV) genotype D8. We established a bench protocol using 1 kb amplicons tiling the MeV WGS. Four different pipeline parameters were assessed based on two basecallers and two quality thresholds: Guppy simplex with Q-score thresholds of 20 and 25 (G20 and G25), and Dorado duplex with Q-score thresholds of 20 and 25 (D20 and D25). Using a reference genome, we determined that complete genomes were obtained down to 10 copies/µL with all four parameters; however, errors began to be detected in the consensus sequence at 100 copies/µL. A panel of specimens from 32 measles cases, for which measles WGS had been obtained by other methods (reference sequences), was used to assess the utility and accuracy of the Oxford Nanopore Technologies (ONT) for the purposes of measles surveillance. We found that a crossing point (Cp) value of 31 (corresponding to approximately 100 copies/µL) or less could be considered a predictor for the generation of accurate and complete WGS. The GQ20 parameter achieved the most complete genomes (75%) and had the most identical sequences (84.4%). Error rates compared with the reference sequences for all parameters were below one nucleotide per whole genome. After assessing the reproducibility, GQ20 had the most identical sequences (97.4%). Finally, we inserted ONT-generated WGS and reference sequences into outbreaks with known epidemiological links, and our results show that the ONT WGS matches the epidemiological data. This evaluation establishes that NGS generated by ONT produces accurate and reliable MeV WGS.IMPORTANCEThe use of ONT-sequencing platforms has the potential to expand the availability of measles sequencing as a result of its relatively lower cost and portability. This study establishes that measles sequences generated by ONT are accurate and reliable. This will enable sequencing in global regions where there is a lack of sequence data (which also tend to be the measles exporting regions) and more timely sequencing in low incidence settings, due also to the lower number of samples needed for the ONT platform. More timely generation of these data enables better investigation of cases, which informs public health response and outbreak management in measles-eliminated countries.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".