regentrans: a framework and R package for using genomics to study regional pathogen transmission
Bibliographic record
Abstract
Abstract Increasing evidence of regional pathogen transmission networks highlights the importance of investigating the dissemination of multidrug-resistant organisms (MDROs) across a region to identify where transmission is occurring and how pathogens move across regions. We developed a framework for investigating MDRO regional transmission dynamics using whole-genome sequencing data and created regentrans, an easy-to-use, open source R package that implements these methods ( https://github.com/Snitkin-Lab-Umich/regentrans ). Using a dataset of over 400 carbapenem-resistant Klebsiella pneumoniae isolates collected from patients in 21 long-term acute care hospitals over a one-year period, we demonstrate how to use our framework to gain insights into differences in inter- and intra-facility transmission across different facilities and over time. This framework and corresponding R package will allow investigators to better understand the origins and transmission patterns of MDROs, which is the first step in understanding how to stop transmission at the regional level. Impact statement Increasing evidence suggests that pathogen transmission occurs across healthcare facilities. Genomic epidemiologic investigations into regional transmission shed light on potential drivers of regional prevalence and can inform coordinated interventions across healthcare facilities to reduce transmission. Here we present a framework for studying regional pathogen transmission using whole-genome sequencing data, and a corresponding open-source R package, regentrans, that implements these methods to streamline analyses and make them more accessible to other researchers and public health practitioners. We also discuss how these methods can be extended to study transmission in other settings. Data summary The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. The regentrans R package can be downloaded from GitHub: https://github.com/Snitkin-Lab-Umich/regentrans/ The manuscript figures are generated from regentrans example data and can also be found on GitHub: https://github.com/Snitkin-Lab-Umich/regentrans/tree/master/vignettes/manuscript_figures The example data used in the package and manuscript is from BioProject accession no. PRJNA415194. The specific SRA accession numbers can be found in supplementary file S1. The metadata corresponding to these sequences can be found on the SRA Run Selector (isolate column) and as example data in the regentrans package. The KPNIH1 sequence was used as the reference genome (SRA accession number SRZ080789)
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.035 |
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".