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other·epidemiology, public health, infectious disease, research methods, food science·PMC10134794

A Schema for Digitized Surface Swab Site Metadata in Open-Source DNA Sequence Databases

mSystems · 8 authors, 4 centres

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TL;DR

This paper proposes a standardized metadata schema to improve the quality and machine-readability of swab site location data in open-source DNA sequence databases used for foodborne pathogen surveillance. The authors demonstrate that digitized metadata can enable quantitative analysis of sampling efforts and pathogen detection.

Full summary

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The authors analyzed existing free-text responses to identify common issues and developed a machine-readable schema organized into five informational facets. The schema is implemented in the One Health Enteric Package to support genomic surveillance of enteric pathogens. The study notes that while upfront standardization requires more effort than free-text, it enhances the efficiency of epidemiological investigations by facilitating data integration and analysis. The work is part of broader efforts toward tech-enabled traceability and is compatible with other ontologies like FoodOn and GenEpio.

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other

SPECIALTY

epidemiology

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ORIGINAL PAPER

PMC10134794

A Schema for Digitized Surface Swab Site Metadata in Open-Source DNA Sequence Databases

mSystems

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ABSTRACT

ABSTRACT Large, open-source DNA sequence databases have been generated, in part, through the collection of microbial pathogens by swabbing surfaces in built environments. Analyzing these data in aggregate through public health surveillance requires digitization of the complex, domain-specific metadata that are associated with the swab site locations. However, the swab site location information is currently collected in a single, free-text, “isolation source”, field-promoting generation of poorly detailed descriptions with various word order, granularity, and linguistic errors, making automation difficult and reducing machine-actionability. We assessed 1,498 free-text swab site descriptions that were generated during routine foodborne pathogen surveillance. The lexicon of free-text metadata was evaluated to determine the informational facets and the quantity of unique terms used by data collectors. Open Biological Ontologies (OBO) Foundry libraries were used to develop hierarchical vocabularies that are connected with logical relationships to describe swab site locations. 5 informational facets that were described by 338 unique terms were identified via content analysis. Term hierarchy facets were developed, as were statements (called axioms) about how the entities within these five domains are related. The schema developed through this study has been integrated into a publicly available pathogen metadata standard, facilitating ongoing surveillance and investigations. The One Health Enteric Package was available at NCBI BioSample, beginning in 2022. The collective use of metadata standards increases the interoperability of DNA sequence databases and enables large-scale approaches to data sharing and artificial intelligence as well as big-data solutions to food safety. IMPORTANCE The regular analysis of whole-genome sequence data in collections such as NCBI’s Pathogen Detection Database is used by many public health organizations to detect outbreaks of infectious disease. However, isolate metadata in these databases are often incomplete and of poor quality. These complex, raw metadata must often be reorganized and manually formatted for use in aggregate analyses. These processes are inefficient and time-consuming, increasing the interpretative labor needed by public health groups to extract actionable information. The future use of open genomic epidemiology networks will be supported through the development of an internationally applicable vocabulary system with which swab site locations can be described. KEYWORDS: genomic surveillance, epidemiology, informatics, foodborne pathogen

CITATION EXPORT

Jingzhang Feng, Devin Daeschel, Damion Dooley et al.. A Schema for Digitized Surface Swab Site Metadata in Open-Source DNA Sequence Databases. mSystems. (2023). https://doi.org/10.1128/msystems.01284-22

AUTHORS

8 · 4 CENTRES

Jingzhang Feng, Devin Daeschel, Damion Dooley, Emma Griffiths, Marc Allard, Ruth Timme, Yi Chen, Abigail B Snyder

aDepartment of Food Science, Cornell University, Ithaca, New York, USA
bCentre for Infectious Disease Genomics and One Health, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
cDivision of Microbiology, Office of Regulatory Science, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, Maryland, USA
dUniversity of California San Diego