Plant & Animal Genomes XIII Conference

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

Using TAIR For Mining Arabidopsis Gene Expression And Metabolic Pathways.

Leonore Reiser¹ , Margarita Garcia-Hernandez¹ , Peifen Zhang¹ , Doug Becker¹ , Tanya Berardini¹ , Ron Caspi³ , Hartmut Foerster¹ , Carol Fulcher³ , Eva Huala¹ , Katica Ilic¹ , Peter Karp³ , Markus Krummenacker³ , Neil Miller² , Mary Montoya² , Nick Mosekyo¹ , Suparna Mundodi¹ , Suzanne Paley³ , Seung Yon Rhee¹ , Julie Tacklind¹ , Danforth Weems² , Christopher Wilks¹ , Iris Xu¹ , Thomas Yan¹ , Daniel Yoo¹ , Brandon Zoeckler¹

¹  Carnegie Institution Department of Plant Biology, 260 Panama St., Stanford, CA .94305

²  National Center for Genome Resources, 2935 Rodeo Park Drive East, Santa Fe, New Mexico, 87505

³  333 Ravenswood Avenue, Menlo Park, CA, 94025-3493

The Arabidopsis Information Resource (TAIR; www.arabidopsis.org) is a comprehensive resource of Arabidopsis biology for plant scientists. TAIR integrates information about genes, proteins, gene expression, mutant phenotypes, biological materials such as DNA and seed stocks, genetic markers, genetic and physical maps, biochemical pathways, genome organization, publications and the research community. The first part of this hands-on workshop will focus on accessing, downloading an analyzing expression Arabidopsis microarray data stored in TAIR. Specifically, we will cover the following tasks: Finding and understanding expression data for gene(s) of interest; Classifying clustered expression data based on functional categorization using the Gene Ontology assignments; Finding over-represented oligomers within a given set of genes using MotifFinder; Finding microarray experimental datasets using the Microarray Experiment Search; Investigating the behavior of sets of genes in a variety of experiments using the Microarray Expression Search; and Mining for similarly expressed genes across a variety of experiments using VxInsight and Java TreeView. In the second part of the workshop we will explore the uses of the Arabidopsis metabolism database, AraCyc, for investigating plant biochemical pathways. Using the Omics Viewer tool in AraCyc, we will analyze sample microarray data to identify changes in the expression of genes involved in metabolic pathways. The viewer can be used to overlay other large-scale data such as those resulting from proteomics and metabolomics experiments. Finally, we will demonstrate how MetaCyc (www.metacyc.org) can be used to predict metabolic pathways in other annotated genomes.