What is GeneTegra?

GeneTegra is a novel integration application that allows users to locate, browse, and query disparate data sources from a single interface. GeneTegra uses Semantic Web standards to resolve the semantic and syntactic diversity of the large and increasingly complex body of publicly available data and private data at research institutes. It performs data linking by leveraging the ASMOV ontology alignment component, a top performing technology at the Ontology Alignment Evaluation Initiative (OAEI) contest, and provides extraction functionalities for heterogeneous data sources.

This is accomplished without the need to create a physical data warehouse and with user-friendly drag-and-drop and direct manipulation graphical interfaces. GeneTegra improves turnaround time and reduces the time spent on manual data management and data abstraction processes. With its on-demand integration and sharing capabilities, GeneTegra enhances team-based research and reduces the existence of data silos.

The Standalone version of GeneTegra offers multifunctional data integration and querying features. The Enterprise version of GeneTegra has been designed for use at large institutes to give researchers and data mangers the ability to retrieve and merge data from distributed databases across the institute and to share the data among investigators. Using the Enterprise version, GeneTegra can be used to establish and run a Quality Control and Assurance program for databases throughout an institution.

Main Features

  • Easily view, create and share data among researchers and scientists throughout an institution
  • Re-purpose data by customizing the views to support new research endeavors
  • Ensure data freshness by distributing the queries among live data stores
  • Resolve inconsistencies between disparate sources through the application of rules
  • Allow access to multiple databases as a single harmonized view
  • Explore and query organizational data more efficiently
  • Utilize consistent terminology to access unfamiliar data
  • Combine personal research data with institutional resources