PAG-XIII  Plant & Animal Genomes XIII Conference

January 15-19, 2005
Town & Country Convention Center
San Diego, CA


P816 : Databases

Gramene Diversity Module: Sharing The Data Behind Germplasm, QTL, And Breeding Studies

Edward S. Buckler1,2,3 , Daniel Born2 , Peter Bradbury3 , Payan Canaran4 , Terry Casstevens3 , Dallas Kroon3 , David E. Matthews1,2 , Susan McCouch2,3 , Junjian Ni2 , Doreen Ware1,4 , Immanuel Yap2 , Wei Zhao4

1  USDA-ARS
2  Dept. of Plant Breeding and Genetics, Cornell University
3  Institute of Genomic Diversity, Cornell University
4  Cold Spring Harbor Laboratory, NY, USA

Over the last decade, thousands of quantitative trait locus studies, germplasm surveys, and molecular maps have been published using a wide variety of germplasm resources. While there has been substantial progress in understanding the population structure, QTL, and the genetic architecture of plants, it has been very difficult to synthesize, share, and/or reanalyze the basic data behind these studies. Currently with the support of NSF and USDA-ARS, the Gramene, Maize, Wheat, and Rice diversity projects are collaborating to build the infrastructure to start sharing this basic molecular and phenotypic diversity data. We have developed a data model and schema (GDPDM; www.maizegenetics.net/gdpdm/), a Java XML-SOAP middleware component (GPDC; www.maizegenetics.net/gdpc/), a sequence alignment-SNP viewer (www.panzea.org/db/snp_alignment/snp_cgi.dev.pl), and an association diversity analysis tool (TASSEL; www.maizegenetics.net/bioinformatics/tasselindex.htm). All the software and schemas are open source. Species specific portals and basic query tools to these data sets are currently available for maize (www.panzea.org) and rice (rice-evolution.plbr.cornell.edu).
Over the next year, we will be working to (1) incorporate more of the data from the various diversity projects, (2) facilitate sharing of data with species specific databases, (3) develop community upload tools for smaller datasets, (4) implement mechanisms to share data with other diversity databases (eg. GRIN and IRRI), and (5) create enhanced query, display, and analysis tools. We encourage community input and collaboration on this effort, so that the largest possible community can access and productively use diversity data.