W389 MaizeCyc:. Metabolic Networks in Maize

Date: Tuesday, January 17, 2012
Time: 4:10 PM
Room: California
Marcela Karey Monaco , Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
Taner Z Sen , USDA -ARS, Ames, IA
Liya Ren , Cold Spring Harbor Laboratory, Cold Spring Harbor, NY
Palitha Dharmawardhana , Oregon State University, Corvallis, OR
Mary Schaeffer , University of Missouri - Columbia, 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
Sushma Naithani , Oregon State University, Corvallis, OR
Jack Gardiner , Iowa State University, Ames, IA
Carolyn J. Lawrence , USDA ARS Iowa State University, Ames, IA
Doreen Ware , Cold Spring Harbor Laboratory-USDA-ARS, Cold Spring Harbor, NY
Pankaj Jaiswal , Oregon State University, Corvallis, OR
MaizeCyc is a catalog of known and predicted metabolic and transport pathways that enables plant researchers to graphically represent the metabolome of maize (Zea mays), thereby supporting integrated systems-biology analysis. Supported analyses include molecular and genetic/phenotypic profiling (e.g., transcriptomics, proteomics, and metabolomics data sets) and their comparison across species, tissues/organs, and developmental stages. 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 resource includes sequence-based associations provided by Gramene, MaizeSequence.org, and MaizeGDB to external database entries from EntrezGene, UniProt-SwissProt, and Gene Ontology. Mapping of classical phenotypes to sequenced genomic loci were obtained from Schnable and Freeling (2011), proteomics-supported gene annotations from Friso et al (2010), and EC code mappings from MaizeGDB Pathways. We used expression-profiling data from the ATLAS transcriptomics data set (Sekhon et al, 2010) to create exemplar visualizations of spacio-temporally regulated global gene expression in maize, and will provide these representations freely to the community. MaizeCyc is currently available at Gramene and MaizeGDB, and as of version 2.0, it will also be included in the SEED website for public view and data download. The database was created using the PathoLogic module of Pathway Tools (ver 15.0). This work is supported by the NSF (Gramene: A Platform for Comparative Plant Genomics), and the USDA-ARS (The Maize Genetics and Genomics Database).