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Title: Multi-source and ontology-based retrieval engine for maize mutant phenotypes

Author
item GREEN, JASON - University Of Missouri
item HARNSOMBURANA, JATURON - University Of Missouri
item Schaeffer, Mary
item Lawrence, Carolyn
item SHYU, CHI-REN - University Of Missouri

Submitted to: Database: The Journal of Biological Databases and Curation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/22/2011
Publication Date: 5/10/2011
Citation: Green, J.M., Harnsomburana, J., Schaeffer, M.L., Lawrence, C.J., Shyu, C. 2011. Multi-source and ontology-based retrieval engine for maize mutant phenotypes. Database: The Journal of Biological Databases and Curation. 2011:Article ID bar012. Available: http://database.oxfordjournals.org/content/2011/bar012.

Interpretive Summary: In the midst of this genomics era, major plant genome databases are collecting massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc., as well as textual descriptions of many of these entities. While basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next generation search mechanisms that utilize text descriptions are nonexistent, as are retrieval engines that leverage existing links in these databases to combine text descriptions of various types of stored data. Current searches of genome databases do not integrate the structure of ontologies (description of the concepts and relationships between genes and plants - the knowledge base for the genome) with full text searching. We describe new computational tools, using MaizeGDB as the testing bed, that solve this problem. This next generation searching utility integrated the heirarchical structure of Plant and Gene Ontologies to search text fields for maize phenotypes, genes and gene products. To our knowledge, this is the first genome database search tool that performs this integration and will greatly improve the research capabilities for maize geneticists and breeders.

Technical Abstract: In the midst of this genomics era, major plant genome databases are collecting massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc., as well as textual descriptions of many of these entities. While basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next generation search mechanisms that utilize text descriptions are nonexistent, as are retrieval engines that leverage existing links in these databases to combine text descriptions of various types of stored data. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB.