INNUENDO: A cross‐sectoral platform for the integration of genomics in the surveillance of food‐borne pathogens
Bibliographic record
Abstract
Abstract In response to the EFSA call New approaches in identifying and characterizing microbial and chemical hazards, the project INNUENDO (https://sites.google.com/site/theinnuendoproject/) aimed to design an analytical platform and standard procedures for the use of whole-genome sequencing in surveillance and outbreak investigation of food-borne pathogens. The project firstly attempted to identify existing flaws and needs, and then to provide applicable cross-sectorial solutions. The project focused in developing a platform for small countries with limited economical and personnel resources. To achieve these goals, we applied a user-centered design strategy involving the end-users, such as microbiologists in public health and veterinary authorities, in every step of the design, development and implementation phases. As a result, we delivered the INNUENDO Platform V1.0 (https://innuendo.readthedocs.io/en/latest/), a stand-alone, portable, open-source, end-to-end system for the management, analysis, and sharing of bacterial genomic data. The platform uses Nextflow workflow manager to assemble analytical software modules in species-specific protocols that can be run using a user-friendly interface. The reproducibility of the process is ensured by using Docker containers and throught the annotation of the whole process using an ontology. Several modules, available at https://github.com/TheInnuendoProject, have been developed including: genome assembly and species confirmation; fast genome clustering; in silico typing; standardized species-specific phylogenetic frameworks for Campylobacter jejuni, Yersinia enterocolitica, Salmonella enterica and Escherichia coli based on an innovative gene-by-gene methodology; quality control measures from raw reads to allele calling; reporting system; a built-in communication protocols and a strain classification system enabling smooth communication during outbreak investigation. As proof-of-concepts, the proposed solutions have been thoroughly tested in simulated outbreak conditions by several public health and veterinary agencies across Europe. The results have been widely disseminated through several channels (web-sites, scientific publications, organization of workshops). The INNUENDO Platform V1.0 is effectively one of the models for the usage of open-source software in genomic epidemiology.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".