Our previous funding round of this project was devoted to creating the underlying data standards and infrastructure to support sharing microarray data. The availability of the MGED (Microarray and Gene Expression Data Society; www.mged.org) standards and infrastructure and our accumulated experience using and evaluating them puts us in an excellent position to deliver tools and resources directly to the bench biologists who are generating high-throughput gene expression data: We now propose to shift our main efforts away from building computational infrastructure - the over-arching purpose of this new proposal is to facilitate scientific discovery by providing bench biologists with the tools they need to effectively share gene expression data and to take advantage of well-annotated gene expression data in their research. Our work to create and promote data sharing standards and resources will have greatest impact when those standards and resources are in common use by biomedical researchers. ? ? The need for and potential benefits of standards for microarray and other high-throughput technologies is clear, yet the positive impact of the standards thus far developed has not yet been fully realized. This is in large part because the standards and tools we have developed still require expert knowledge, yet the targeted users are bench biologists who are not experts in this domain, and should not be expected to become so. In this application, in addition to building the next generation of data exchange standards, we are proposing to use the infrastructure we have built in the previous round of funding as the basis for data exchange resources that are useful and usable by bench biologists. Therefore, our aims are to: ? 1. Develop tools to help researchers easily annotate microarray experiments. ? 2. Extend popular data analysis and visualization tools (BioConductor, MeV, GenePattern, Java TreeView) to use MAGE-TAB-encoded experimental annotations. ? 3. Generalize the MAGE-TAB data exchange standard to work with other high-throughput biomedical data, such as ultra-high-throughput sequencing. ? 4. Provide biologist-friendly ontology terms that can be used to annotate microarray data as well as serve as meaningful terms in computational analyses. ? 5. Participate in outreach, education and information gathering efforts to engage the community in the development of standards and to ensure widespread use and critiques. ? ? ? ?

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Biotechnology Resource Grants (P41)
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Study Section
Ethical, Legal, Social Implications Review Committee (GNOM)
Program Officer
Good, Peter J
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Stanford University
Schools of Medicine
United States
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