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Open Access Research article

Identification of nutrient partitioning genes participating in rice grain filling by singular value decomposition (SVD) of genome expression data

Abraham Anderson12, Matthew Hudson12, Wenqiong Chen12 and Tong Zhu13*

Author Affiliations

1 Torrey Mesa Research Institute, Syngenta Research and Technology, 3115 Merryfield Row, San Diego, CA 92121, USA

2 Current Address: Diversa Corporation, 4955 Directors Place, San Diego, CA 92121, USA

3 Current Address: Syngenta Biotechnology Inc., 3054 Cornwallis Road, Research Triangle Park, NC 27709, USA

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BMC Genomics 2003, 4:26  doi:10.1186/1471-2164-4-26

Published: 10 July 2003

Abstract

Background

In order to identify rice genes involved in nutrient partitioning, microarray experiments have been done to quantify genomic scale gene expression. Genes involved in nutrient partitioning, specifically grain filling, will be used to identify other co-regulated genes, and DNA binding proteins. Proper identification of the initial set of bait genes used for further investigation is critical. Hierarchical clustering is useful for grouping genes with similar expression profiles, but decreases in utility as data complexity and systematic noise increases. Also, its rigid classification of genes is not consistent with our belief that some genes exhibit multifaceted, context dependent regulation.

Results

Singular value decomposition (SVD) of microarray data was investigated as a method to complement current techniques for gene expression pattern recognition. SVD's usefulness, in finding likely participants in grain filling, was measured by comparison with results obtained previously via clustering. 84 percent of these known grain-filling genes were re-identified after detailed SVD analysis. An additional set of 28 genes exhibited a stronger grain-filling pattern than those grain-filling genes that were unselected. They also had upstream sequence containing motifs over-represented among grain filling genes.

Conclusions

The pattern-based perspective that SVD provides complements to widely used clustering methods. The singular vectors provide information about patterns that exist in the data. Other aspects of the decomposition indicate the extent to which a gene exhibits a pattern similar to those provided by the singular vectors. Thus, once a set of interesting patterns has been identified, genes can be ranked by their relationship with said patterns.