Field-based detection of bacteria using nanopore sequencing: Method evaluation for biothreat detection in complex samples
Bibliographic record
Abstract
From pathogen detection to genome or plasmid closure, the utility of the Oxford Nanopore Technologies (ONT) MinION for microbiological analysis has been well documented. The MinION's small footprint, portability, and real-time analytic capability situates it well to address challenges in the field of unbiased pathogen detection, as a component of a security investigation. To this end, a multicenter evaluation of the effect of alternative analytical approaches on the outcome of MinION-based sequencing, using a set of well-characterized samples, was explored in a field-based scenario. Three expert scientific response groups evaluated known bacterial DNA extracts as part of an international first responder (Chemical, Biological, Radiological) training exercise. Samples were prepared independently for analysis using the Rapid and/or Rapid PCR sequencing kits as per the best practices of each of the participating groups. Analyses of sequence data were in turn conducted using varied approaches including ONTs What's in my pot (WIMP) architecture and in-house computational pipelines. Microbial community composition and the ability of each approach to detect pathogens was compared. Each group demonstrated the ability to detect all species present in samples, although several organisms were detected at levels much lower than expected with some organisms even falling below 1% abundance. Several 'contaminant' near neighbor species were also detected, at low abundance. Regardless of the sequencing approach chosen, the observed composition of the bacterial communities diverged from the input composition in each of the analyses, although sequencing conducted using the rapid kit produced the least distortion when compared to PCR-based library preparation methods. One of the participating groups generated drastically lower sequencing output than the other groups, likely attributed to the limited computer hard drive capacity, and occasional disruption of the internet connection. These results provide further consideration for conducting unbiased pathogen identification within a field setting using MinION sequencing. However, the benefits of this approach in providing rapid results and unbiased detection must be considered along with the complexity of sample preparation and data analytics, when compared to more traditional methods. When utilized by trained scientific experts, with appropriate computational resources, the MinION sequencing device is a useful tool for field-based pathogen detection in mixed samples.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".