|Alkharouf, N - GEORGE MASON UNIVERSITY|
Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: March 22, 2004
Publication Date: March 22, 2004
Citation: Alkharouf, N., Matthews, B.F. 2004. Identification of resistant genes and pathways in soybean against the cyst nematode using bioinformatics and data mining tools [abstract]. BARC Poster Day. Abstract #06. Technical Abstract: The soybean cyst nematode (SCN; Heterodera glycines Ichinohe) is the major pest of soybean and is responsible for an estimated loss of over one billion dollars per annum in the United States. The genome of soybean is not completely sequenced and few functional genomic studies have been conducted on the interaction of soybean with the soybean cyst nematode. We apply bioinformatics and data mining tools to analyze DNA microarray data and soybean ESTs to find genes and pathways involved in the resistance of soybean to SCN. We found a number of candidate resistant genes, and found the salicylic acid induced pathway to interact with the jasmonic acid and other pathways to confer resistance in soybean against SCN. This is the world's first soybean microarray database and was constructed to store microarray data and soybean ESTs for public use. Analysis tools were integrated in to this database for faster analysis of DNA microarray experiments. Online analytical processing (OLAP) was used to mine the microarray experiments and was found to be an efficient and fast method to mine gene expression data. This database and online analytical tools should be of use to scientists working with soybean and its pests and pathogens.