RDA/IGAD Webinar Series: ‘Data Consistency, Exchange and Interoperability with Tripal v3 -- a platform for genomics, genetics and breeding community databases’

27/05/2020

As part of The Research Data Alliance (RDA) Interest Group on Agricultural Data (IGAD) meeting focusing on IGAD/RDA : Sharing Experiences and Creating Digital Dialogues, Stephen Ficklin, Department of Horticulture, Washington State University presented a webinar during the ‘Americas/Asia’ group of presentations on May 27. 

The webinar focused on ‘Data Consistency, Exchange and Interoperability with Tripal v3 -- a platform for genomics, genetics and breeding community databases.’ Tripal is an open-source platform that can be used to create online biological databases that house genomics, genetics, breeding and ancillary data for non-model organisms.  Often small communities desire to provide online resources for non-model species but without the resources typically available to model-organism databases. Tripal is maintained by an international group of core developers and contributors who share code, database structure and approaches to help reduce duplication of effort, decrease costs with the expectation of providing a high-level of service.  Efforts of this group include infrastructure to provide greater consistency across modes of delivery (e.g. web browsers, web services, etc.) and to foster data exchange and interoperability amongst Tripal (and other) repositories. Such tools and resources are now available in Tripal v3.  Moreover, a "middelayer" data model affords flexible ontology-based data modeling that could serve as a model for data exchange across Tripal and non-Tripal biological databases.

The Research Data Alliance (RDA) Interest Group on Agricultural Data (IGAD) meeting was set to be held in Rome (Italy) in April 2020 but instead, took place as a virtual meeting from May 25 to 28.  The virtual meeting provided an online platform to continue to build upon collaborations, knowledge sharing and developing innovations with activities including panel sessions, group discussions and presentations.

Stephen Ficklin
Areas of Interest Development of computational methods for discovery of molecular biosignatures of complex traits in agricultural plants. Methods include machine learning, deep learning and systems-level multiplex networks of multiomics data sets. Cyberinfrastructure development supporting transfer, storage, visualization and analysis of large genomics and other “omics” data sets. Educational Background Ph.D. Plant and Environmental Sciences, Clemson University (2013) M.S. Computer Science, Clemson University (2003) B.S. Computer Science, Brigham Young University (2000) Teaching Responsibilities HORT 503 – Special Topics: Introduction to Data Analysis in Systems Biology AFS 505 – Topics in Computational and Analytical Methods for Scientists

Become a member

As a member of AIMS, you can contribute to discussions and periodically receive updates via email and the AIMS newsletter

REGISTER