Methods and tools for integrative meta-analysis of neuroscience micro array data

Dozens of neuroscience laboratories around the world are using gene expression microarrays, a technology that simultaneously monitors the activities of thousands of genes in a sample of brain tissue or cells. While the specific goals of each study may vary, a common theme is increasing our understanding what happens to the brain when it is diseased (as in Alzheimer’s disease or schizophrenia) or damaged by injury (such as stroke). These studies each generate huge amounts of data, with the potential for new discoveries arising from the compilation and comparison of results across laboratories. However, there have been few efforts to date to provide advanced analytic capabilities that can span data sets, and none that address the specific needs of neuroscience. Dr. Paul Pavlidis is developing methods, databases and software to gather, integrate and compare the vast amount of data compiled from neuroscience-related gene expression data. The tools he is developing will allow brain researchers to submit their own data, compare it to published data or that of their collaborators, and combine microarray data with other types of gene expression data. This work will help researchers share data and collaborate in studies that target diseases of brain function.