Location: Corn Insects and Crop Genetics ResearchTitle: Soybean transcriptome analysis using the SoyBase soybean metabolic database SoyCyc Author
Submitted to: Plant and Animal Genome Conference
Publication Type: Abstract Only
Publication Acceptance Date: 1/15/2011
Publication Date: 1/15/2011
Citation: Nelson, R., Grant, D.M., Cannon, S.B., Chi, A., Zhang, P., Rhee, S., Shoemaker, R.C. 2011. Soybean transcriptome analysis using the SoyBase soybean metabolic database SoyCyc [abstract]. Plant and Animal Genome Conference. Poster 772. Interpretive Summary:
Technical Abstract: SoyBase, the USDA-ARS soybean genetics and genomic database is a resource for soybean breeders and geneticists. It incorporates both genetic maps and genomic sequence for soybean , as well as marker data, gene models, EST mappings from other legumes, and other genome features. Transcriptome analyses of soybean can be associated with predicted genes in various ways including use of the Affimetrix SoyChip array. These analyses usually produce lists of genes that are deemed to be differentially regulated. The ability to project such a list of genes on to metabolic pathways could help researchers infer functional or causal relationships by which they can formulate testable hypotheses. SoyCyc, a new service of SoyBase, allows users to take the results of their transcriptome analyses and see them in the context of soybean metabolic pathways. In SoyCyc, plant metabolic pathways and their individual reactions have been annotated with soybean gene-calls. This allows users to see if any of their significantly regulated genes participate in metabolic pathways and which steps they catalyze. Additionally, the user can see the magnitude of the signals from each experiment “painted” on to the pathways in colors representing the relative expression values associated with each gene. Users of the database can either browse or search for individual genes/reactions/pathways or can upload files of simply formatted results of transcriptome analyses to display soybean metabolic pathways “painted” with those results. Examples of these files and use cases are presented in the poster.