TRIAD: The Translational Research Informatics and Data Management Grid

Abstract

Objective: Multi-disciplinary and multi-site biomedical research programs frequently require infrastructures capable of enabling the collection, management, analysis, and dissemination of heterogeneous, multi-dimensional, and distributed data and knowledge collections spanning organizational boundaries. We report on the design and initial deployment of an extensible biomedical informatics platform that is intended to address such requirements.

Methods: A common approach to distributed data, information, and knowledge management needs in the healthcare and life science settings is the deployment and use of a service-oriented architecture (SOA). Such SOA technologies provide for strongly-typed, semantically annotated, and stateful data and analytical services that can be combined into data and knowledge integration and analysis "pipelines." Using this overall design pattern, we have implemented and evaluated an extensible SOA platform for clinical and translational science applications known as the Translational Research Informatics and Data-management grid (TRIAD). TRIAD is a derivative and extension of the caGrid middleware and has an emphasis on supporting agile "working interoperability" between data, information, and knowledge resources.

Results: Based upon initial verification and validation studies conducted in the context of a collection of driving clinical and translational research problems, we have been able to demonstrate that TRIAD achieves agile "working interoperability" between distributed data and knowledge sources.

Conclusion: Informed by our initial verification and validation studies, we believe TRIAD provides an example instance of a lightweight and readily adoptable approach to the use of SOA technologies in the clinical and translational research setting. Furthermore, our initial use cases illustrate the importance and efficacy of enabling "working interoperability" in heterogeneous biomedical environments.

Keywords: Clinical research informatics; data access; data analysis; data integration; socio-organizational issues; standards; workflow.

Figure 1

Overview of four caGrid design and key design and functional aspects that correspond to data and knowledge sharing requirements present in the contemporary clinical and translational research environment.

Figure 2

Overview of workflow culminating in the creation and use of TRIAD data services targeting underlying relational database constructs, involving the following major steps: 1) creation of UML models that map to existing relational data structures; 2) annotation of UML models using with semantic metadata; 3) the semi-automated generation of Java-based grid adapters using the caCORE SDK and Introduce toolkit; and 4) the implementation and deployment of resulting grid services using the TRIAD-specific instance of the caGrid middleware.

Figure 3

Overview of openMDR components, including an ISO 11179 compliant metadata database, enterprise modeling tools and annotation plug-ins, grid-service domain model generator, and federated metadata query processor.

Figure 4

Overview of tissue cohort discovery tool implementation, in which: 1) end users pose a query via a cohort discovery portal built as a derivative of the caGRID portal platform; 2) that query is distributed and executed using Distributed Common Query Language (DCQL) via a TRIAD-specific instance of the caGrid-developed Federated Query Processor (FQP); 3) the ensuing source-specific queries, as specified via the initial DCQL statement and related semantic metadata and object modes, is executed against source systems; and 4) aggregate cohort-specific result sets are communicated to the portal interface and presented to the end user from FQP. In this example instance, phenotype data is being retrieved from the OSUMC IW, and biospecimen management data is being retrieved from a project-specific instance of caTissue Suite.