Needs Assessment and Ontology Development for Integrating Whole Genome Sequencing into Routine Outbreak Investigations
Bibliographic record
Abstract
Whole-genome sequencing (WGS) can provide increased typing discriminatory power as well as additional data analyzed from genomic content using comparative genomics tools. Canada’s Integrated Rapid Infectious Disease Analysis (IRIDA) platform will equip public health workers with user friendly tools for incorporating WGS into isolate typing and epidemiological pipelines to support real-time infectious disease investigation. In order to understand the practical requirements for implementing this platform within the Canadian health care network, a needs assessment was conducted by interviewing public health stakeholders and domain experts. User activities, lab management software, national pathogen-tracking and reporting systems were profiled to better characterize levels of user computational expertise, required functionalities and information flow. Key gaps were identified in personnel training, data processing and sharing, data integration, as well as governance. Consistent capture and structured submission of metadata is crucial for integrated data analyses, human health risk assessments, source attribution, ecosystems modelling, and in the simplest terms, to make sense of the genome data. In addition to the needs assessment, an ontology resource review was conducted to assess the utility of different community standards for fulfilling the needs of a genomic epidemiology program. No single ontology is sufficient to cover all attributes required for genomic epidemiology and the very breadth of many ontologies hinders their practical use in real-time by users with little bioinformatics expertise. User profiles and data requirements were harmonized with different sources in order to produce an OWL file containing metadata fields and terms describing isolate source attribution, lab analytics, sequencing/assembly/annotation processes as well as quality metrics, and patient demographics. The data integration capabilities of the IRIDA ontology are currently being tested in a Canadian government Genome Research and Development Initiative and will shortly be expanded to include antimicrobial resistance. By adhering to the best practices of the Open Biomedical and Biological Ontology (OBO) Consortium, our model allows consolidation of various existing ontological efforts into a resource directly compatible with IRIDA. This research is a key component of the IRIDA platform allowing for automated data integration alleviating the burden of manual analyses and triggering action after auto-detecting deviations above expected biosurveillance baselines. The lessons learned from the needs assessment and ontology development reported here should be informative for other countries with autonomous health regions.
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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 | 0.048 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".