Building a Network of Plant Metabolic Pathway Databases and Communities

Carnegie Institution Of Washington, Washington DC

Investigators

Abstract

The Carnegie Institute of Washington is awarded a grant to create a network of plant metabolism databases. At the center of the network will be PlantCyc containing pathways from many plants, supported by experimental evidence for pathways, reactions or enzymes. PlantCyc will be initialized from currently available plant metabolism databases such as AraCyc, TomatoCyc, RiceCyc, MedicagoCyc and Soybase. It will be used as a reference database (in conjunction with MetaCyc) to create multiple plant pathway genome databases (PGDBs) with substantial sequence data. To build a PGDB, putative enzyme sequences will be identified for each organism using several sequence analysis methods and Pathway Tools software will be used to generate the initial PGDBs from the annotated sequences. As each PGDB is built, all of the pathways and enzymes in the new PGDB will be validated and added to PlantCyc, and subsequently curated. Therefore, with each round of PGDB prediction, the quantity and quality of PlantCyc will be increased. The project will leverage the curation teams at other databases interested in different species as well as biochemistry experts who are interested in specific domains of metabolism in improving the content of PlantCyc and the PGDBs. All of the data will be made freely available and updates will be released on a regular basis. As the worldwide demand for production of biofuels, food, animal feed and new medicines continues to grow, there is an increasingly urgent need to develop new technologies using plants. The long-term goal of developing these technologies has prompted the sequencing of plant genomes and gene complements. There is a growing need to place the sequenced and annotated genomes in a biochemical context in order to facilitate discovery of enzymes and engineering of metabolism. This proposal will generate an infrastructure for comprehensive plant metabolism information that addresses the need to store, analyze and display the growing body of data that is emerging from both conventional biochemistry and high-throughput/large-scale data experiments. The proposed network of databases will facilitate the discovery of new enzymes and pathways, the engineering of metabolic pathways, and the curation of new findings in the context of overall metabolic scheme of an organism.