Using TAIR For Mining Arabidopsis Gene Expression And Metabolic Pathways
This will be a hands-on tutorial on using TAIR.
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
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 behavi or 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.
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This page last updated Saturday, 23-Oct-2004 16:34:45 EDT