|Shyu, Chi Ren -|
|Harnsomburana, Jaturon -|
|Green, Jason -|
|Barb, Adrian -|
|Kazic, Toni -|
|Coe, Edward -|
Submitted to: Journal of Bioinformatics and Computational Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 13, 2007
Publication Date: December 1, 2007
Citation: Shyu, C., Harnsomburana, J., Green, J., Barb, A.S., Kazic, T., Schaeffer, M.L., Coe, E.J. 2007. Searching and Mining Visually Observed Phenotypes of Maize Mutants. Journal of Bioinformatics and Computational Biology. 5(6):1193-1213. Interpretive Summary: High-throughput computational analysis of phentotypic variation in the field will allow relating subtle variations in traits important to plant breeders to their genetic basis and to biologists investigating basic plant processes. Currently evaluation is done by eye, either against a 1-10 scale, for example in descriptions of reponses to pests, or by jotting down some notes in the field, along with a quick photo. In addition to being very subjective, this strategy varies among research groups examining similar phenotypes, both at the same location and other locations with different environments. We decribe a strategy that couples data-mining of high quality images with a genome database, MaizeGDB. While the current examples exploited are in maize, the strategy is generic for any plant species, and then can be used to identify agronomically favorable variation in related plant species that might be explored towards breeding improvements in a target crop.
Technical Abstract: There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the ability to recognize novel phenotypes or classify known phenotypes, and an intimate knowledge of the biochemical processes generating physiological and phenotypic effects. They must also know how all of these factors change and differ among species, diverse alleles, germplasms, and environmental conditions. While there are robust databases, such as MaizeGDB, for some of these types of raw data, other crucial components are missing. Moreover, the management of visually observed mutant phenotypes is still in its infant stage, let alone the complex query methods that can draw upon high-level and aggregated information to answer the questions of geneticists. In this paper, we address the scientific challenge and propose to develop a robust framework for managing the knowledge of visually observed phenotypes, mining the correlation of visual characteristics with genetic maps, and discovering the knowledge relating to cross-species conservation of visual and genetic patterns. The ultimate goal of this research is to allow a geneticist to submit phenotypic and genomic information on a mutant to a knowledge base and ask, "What genes or environmental factors cause this visually observed phenotype?"