P0858 MaizeCyc: Metabolic Networks in Maize

Marcela Karey Monaco , Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
Taner Z Sen , USDA -ARS, Ames, IA
Palitha Dharmawardhana , Oregon State University, Corvallis, OR
Liya Ren , Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
Mary Schaeffer , USDA-ARS and University of Missouri, Columbia, MO
Vindya Amarasinghe , Oregon State University, Corvalis, OR
Jim Thomason , Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
Lisa C Harper , University of California - Berkeley, Berkeley, CA
Jack Gardiner , Iowa State University, Tucson, AZ
Carolyn J. Lawrence , USDA ARS Iowa State University, Ames, IA
Doreen Ware , Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
Pankaj Jaiswal , Oregon State University, Corvallis, OR
MaizeCyc is a Pathway Genome Database, and represents a catalog of known and predicted metabolic and transport pathways for corn (Zea mays), which enables plant researchers to study and graphically represent the metabolome of this cereal, thereby supporting integrated systems-biology analysis. MaizeCyc is accessible from the Gramene and MaizeGDB websites, and was created using Pathway Tools software version 15. Analyses and cross-species comparisons are supported for a variety of data, including genetic and phenotypic profiles, transcriptomics, proteomics, and metabolomics data sets. Pathways, reactions, and genes in the catalog are based on the electronic and manual annotations of 39,656 maize gene models in the RefGen_v2 filtered set (Maize Genome Sequencing Project, release 5b.60), and phylogenetically-derived projections from related plant models, including Arabidopsis and rice. This community resource includes sequence-based associations provided by Gramene, MaizeSequence, and MaizeGDB to external database entries from EntrezGene, UniProt-SwissProt, and Gene Ontology. Manual annotations of genes include mapping of classical phenotype genes to sequenced genomic loci, proteomics-supported functional annotations, and enzyme commission code mappings from literature mining. Using expression profiling data from the B73 Maize Gene Atlas transcriptomics set, we created exemplar visualizations of spacio-temporally regulated global gene expression in maize, and will provide these graphic representations freely to the community. This work is supported by the NSF (Gramene: A Platform for Comparative Plant Genomics), and the USDA-ARS (The Maize Genetics and Genomics Database).