An Extensible Semantic Bridge between Biodiversity and Genomics

University Of Nebraska-Lincoln, Lincoln NE

Investigators

Abstract

The University of Nebraska is awarded a grant to develop the Semantic Cyberinfrastructure for Investigation and Discovery (SCID), a general-purpose Web-based ontological software infrastructure that (1) unites numerous biological research interests by enabling seamless federation of heterogeneous databases from each area; (2) supports scientists in the efficient and effective use of ontologies to enhance investigation and discovery; and (3) streamlines the use of bioinformatics tools via common user interfaces for querying data, enhanced data analyses, and interpretation of scientific results, thus enabling discovery by those who enter or cross over into new research fields. Leveraging its long-standing collaboration between faculty from genomics, museum science, and computer science, the SCID team will enhance its existing prototype by (1) developing a "seed" ontology that unites multiple selected databases in biodiversity and genomics, (2) providing an application programming interface (API) to allow new and existing bioinformatics tools to easily "plug in" to our system, (3) developing tools to facilitate the process of reaching scientific consensus in open forums through collaborative editing technologies, and (4) developing tools to support ontological development, federation of disparate data stores, and decision making during investigation and discovery activities. This project will advance genomics and biodiversity research by federating and unifying data sources in biodiversity and genomics, unlocking heterogeneous, largescale databases, and empowering investigation and discovery. It will also advance the computational areas of machine learning, multiagent systems, and semantic technologies for extracting information from large data repositories, supporting users in making investigations and discoveries, and facilitating community-based knowledge engineering. New computational models that are developed as part of SCID will facilitate the building of ontologies that tie together disparate databases, support automatic inferences and deductions, allow for data consistency checks, and allow posing of sophisticated queries, thus creating an innovative, dynamic scientific structure.