Arthritis Genomics and Bioinformatics

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The overall goal of the Arthritis Genomics and Bioinformatics Core will be to support investigators in the Program utilizing microarray techniques. The core will provide uniform sample preparation, microarray design, data storage and bioinformatics, thus maximizing the reliability and compatibility of information obtained across the Program. The Core will perform standardized RNA preparation from joint tissues of experimental animals, and the amplification steps to generate labeled probe from small amounts of in vivo or in vitro derived RNA. This should help to generate highly comparable datasets throughout the Program. In addition, the Core will design a study-specific "Arthritis Chip", with spotted oligonucleotides that represent genes that vary during arthritogenesis. These custom chips will provide flexibility, allow a greater throughput than would be economically feasible with whole-genome chips, and again enhance data compatibility between the groups. The Core's bioinformatics personnel, which have acquired significant experience in microarray analysis, will guide, train, and help investigators through data analysis in an open and collaborative manner. The Core will provide access to basic analysis packages, and the ability to write custom software in a data-driven manner, in response to particular experimental situations. In addition, data analysis will benefit from a baseline of microarray data on gene expression during arthritis. A central server for secure data storage will house copies of the data, and Web-based software for data browsing will be applied as a Program-specific intranet and for public posting, as appropriate. In addition, the Core will perform data mining on the assembled data, explorations made possible by the coordinated processing and storage of the data, and by our pre-existing data on the evolution of gene expression in arthritic joints. Cross-experiment analyses will search for gene-gene correlations and clusters throughout a variety of conditions, and will generate functional gene hierarchies. These meta-analyses will enrich each of the individual analyses: defining, for example, the subset of genes that are still induced in spite of a genetic blockade of arthritis will help to pinpoint the cellular or functional level at which the gene is implicated. In addition, the meta-analyses made possible by the combined and coordinated datasets will also provide a unique insight into the "arthritis genome".