- About the PHA4GE Wastewater Contextual Data Specification
- Integration with the international wastewater surveillance ecosystem?
- What is genomics contextual data and why is it important?
- Existing standards and creating a framework for interoperability
- The Wastewater Contextual Data Specification Package
- Contacts and licensing
About the PHA4GE Wastewater Contextual Data Specification: An interoperable framework for wastewater microbial genomics environmental surveillance
Wastewater genomics data is increasingly being used to support a wide variety of surveillance programs and research to support public health decision making and responses e.g. detection and monitoring of emerging and existing pathogens, detection and characterization of antimicrobial resistance (AMR) determinants, quantifying infectious disease genotypes and assessing prevalence, etc. Wastewater genomic surveillance integrates diverse data from various sources, agencies, and systems, presenting challenges in harmonization, integration and interpretation. Structuring contextual data using data standards, aids accessibility and usability by both humans and computers supporting reuse.
The PHA4GE Wastewater Contextual Data Specification Package is scoped for data collection and sharing (within organizations, within networks and if desired, with public repositories) of both pathogen-agnostic genomics contextual data and genotypic attributes (such as antimicrobial resistance genes) derived from amplicon-based, WGS, and metagenomic sequencing approaches. The goal of the specification is to create a data interoperability framework that enables exchange and communication between data generators and consumers using wastewater for surveillance, that is extensible for other types of water-based surveillance (e.g. agriculture-based water monitoring, freshwater and marine environmental studies, and wildlife vector investigations), and is compatible with existing clinical and One Health standards. The specification was designed through consultation with partners involved in different wastewater projects in LMICs and HICs. The specification package, when completed, will consist of:
- Ontology-based, standardized terminology implemented in public health-ready templates and tools
- Supporting materials including terminology reference guides, a data curation SOP (containing ethical, privacy, and practical considerations for data sharing), and submission protocols
- Mapping schemes between commonly used wastewater standards specifying an interchange format, and tools enabling automated data transformations between the different standards (under development)
- A new term request system enabling users to request new fields and terms if vocabulary is missing and as data needs evolve
This is a resource co-developed by PHA4GE, the Centre for Infectious Disease Genomics and One Health (CIDGOH), the Public Health Agency of Canada, and the Bill & Melinda Gates Foundation to support wastewater microbial genomics-based surveillance and research.
- Labs without data standards can adopt the PHA4GE standard.
- Labs that have their own standards may also use the PHA4GE interoperability framework to automate data transformations to facilitate data sharing and communication with other labs that are using different standards.
- As PHA4GE maintains its resources and evolves its standards over time, labs can communicate needs to PHA4GE and contribute to maintenance and development (and get credit for their contributions) of standards via consensus.
- PHA4GE continues to work with international databases to make the standard available to labs for harmonization of public data.
Microbial wastewater genomics data is composed of sequence data and contextual data. Contextual data consists of the sample metadata, as well as the environmental conditions and measurements, methods, and provenance information needed to attribute, analyze, and make sense of the sequence data. Wastewater genomics data is increasingly utilized in various surveillance programs and research, aiding public health decision-making and responses: e.g., detection and monitoring of emerging and existing pathogens, detection and characterization of antimicrobial resistance (AMR) determinants, quantifying infectious disease genotypes and assessing prevalence, etc. These activities often require information about sample types (environmental sites and materials), surveillance and sampling scope (target organisms, sampling strategies, wastewater systems, frequency and duration of testing, devices and methods of collection), sequencing and enrichment methods (amplicon sequencing, WGS, metagenomics; filtering; sequencing instrument), bioinformatics processing and quality control metrics (software, pipelines, reference databases, coverage, total/mapped reads), as well as contributor information for establishing chains of custody and for follow-up.
Wastewater genomic surveillance often involves data streams originating from different sources, agencies, sectors, and information management systems. These data are generally structured in a variety of ways posing challenges for data harmonization, integration and meaningful interpretation. By structuring contextual data using data standards, this information can be more easily understood and used by both humans and computers and can be more easily reused for different types of analyses. Standards providing guidance on which data elements are the most informative for different public health analyses, as well as any issues regarding their privacy and sensitivity, is also critical.
During the pandemic, PHA4GE developed a pathogen genomics framework for clinical SARS-CoV-2 contextual data in collaboration with experts from around the world, and iteratively improved it via testing in real-world HIC/LMIC settings. The standard was implemented by different laboratories internationally, and both utilized and contributed to different community specifications (e.g. NCBI SARS-CoV-2 Biosample package, >23 different OBO Foundry ontologies, GSC MIxS checklist, GSCID/BRC Project and Sample Application Standard). The framework has since been adapted and repurposed for other surveillance initiatives. PHA4GE has expanded this framework for wastewater and metagenomics, beyond its initial SARS-CoV-2 amplicon-based sequencing scope.
This framework is structured using an ontology approach: ontologies are hierarchies of controlled vocabulary developed by domain area experts and maintained through consensus for, and by, the community. As such, the meanings of terms are meant to be universal rather than institution- or project-specific and are disambiguated using universal identifiers known as Internationalized Resource Identifiers (IRIs). With the emphasis on common meaning rather than relying solely on term labels, ontologies incorporate synonyms and database mappings so that there is no “one-size fits all'' terminology or nomenclature system, contributing to interoperability. Communities of practice like the OBO Foundry articulate and implement best principles and practices to enable reuse of terminology across domains and sectors. Furthermore, there are different community registries and portals enabling FAIR ontology development and implementation (EBI OLS, Ontobee, BioPortal), as well as data modeling languages (LinkML, OWL, RDF) and tools for improving reuse and interoperability (Protégé, ROBOT, OntoFox).
A key principle for data harmonization is that there is no one-size-fits-all standard. Different standards will have different strengths and intended uses. As such, empowering reuse of transferable elements and automated data transformations between these standards will be critical for enabling data harmonization.
In order to put standards into practice, it is necessary to operationalize them with accessible, easy-to-deploy, easy-to-use tools. The DataHarmonizer, s a template-driven spreadsheet application for harmonizing, validating and transforming genomics contextual data into submission-ready formats for public or private repositories. This tool offers a number of curation features and validation. The PHA4GE Wastewater specification was engineered as a set of three relevant templates in the DataHarmonizer:
- SARS-CoV-2 surveillance template
- Antimicrobial resistance detection template
- Metagenomic based single pathogen surveillance template
These wastewater templates can be found in the Pathogen Genomics Package. Required fields are colour-coded yellow, recommended fields are coloured purple, and optional fields are white. The DataHarmonizer provides user tutorials as well as reference guides for fields and terms. Accompanying Field and Term reference guides (which provide definitions and additional specific guidance) and a curation Standard Operating Procedure (SOP) listed below.
New terms and/or term changes can be requested using issue request forms, with additional guidance on how to do so outline in the New Term Request (NTR) SOP.
- Pathogen Genomics Package (wastewater)
- Template schema files can be found as
.yaml/.json/.tsvunder pathogen-genomics-package/templates/wastewater
- Template schema files can be found as
- DataHarmonizer App
- The DataHarmonizer is a standardized browser-based spreadsheet editor and validator.
- Instructions on "Getting Started" downloading and using the application can be found under DataHarmonizer Instructions and SOP below.
- Further information about application functionality can be found on the DataHarmonizer Wiki.
- Curation SOP PDF version and online version
- DataHarmonizer SOP PDF version and online version
- New Term Request SOP PDF version and online version
Please note that development of the specification is dynamic and it will be updated periodically to address user needs. Versioning is done in the format of x.y.z.
x = Field level changes
y = Term value / ID level changes
z = Definition, guidance, example, formatting, or other uncategorized changes
Descriptions of changes are provided in release notes for every new version.
Contacts
For more information and/or assistance, contact [email protected] or submit a repository issue request.
License
Pending / To Be Determined
Acknowledgements
Brought to you by the Centre for Infectious disease Genomics and One Health and the Public Health Alliance for Genomic Epidemiology Data Structures Workgroup.
