Nanopore Metagenomic Sequencing for Detection and Characterization of SARS-CoV-2 in Clinical Samples
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has underscored the need for rapid novel diagnostic strategies to detect and characterize pathogens from clinical specimens. The MinION sequencing device allows for rapid, cost-effective, high-throughput sequencing; useful features for translation to clinical laboratory settings. Metagenomic Next-Generation Sequencing (mNGS) approaches provide the opportunity to examine the entire genomic material of a sample; allowing for detection of emerging and clinically relevant pathogens that may be missed in targeted assays. Here we present a pilot study on the performance of Sequence-Independent Single Primer Amplification (SISPA) to amplify RNA randomly for the detection and characterization of SARS-CoV-2. We designed a classifier that corrects for barcode crosstalk between specimens. Our assay yielded 100% specificity overall and 95.2% sensitivity for specimens with a RT-qPCR cycle threshold value less than 30. We assembled 10 complete (>95% coverage at 20x depth), and one near-complete (>80% coverage at 20x depth) genomes from 20 specimens that were classified as positive by mNGS. We characterized these genomes through phylogenetic analysis and found that 10/11 specimens from British Columbia had a closest relative to another British Columbian specimen. Of five samples that we had both assembled genomes, as well as Variant of Concern (VOC) PCR results, we found 100% concordance between these results. Additionally, our assay was able to distinguish between the Alpha and Gamma variants, which was not possible with our VOC PCR technique. This study supports future work examining the broader feasibility of SISPA as a diagnostic strategy for the detection and characterization of viral pathogens.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".