Great Lakes Bioinformatics Conference (GLBIO) 2011
Advances in microarray technology have led to highly complex datasets often addressing similar or related biological questions. The statistical methodology of meta-analysis aims to combine results from independent but related studies. It is a relatively inexpensive option that has the potential to increase both the statistical power and generalizability of single-study analyses. For example, a meta-analysis of five circadian microarray studies of Drosophila helped researchers to identify a novel set of rhythmically expressed genes. We advocate here a related approach to potentially extend confirmed results to other species or organs. In translational medicine or biology research is often based on measurements that have been obtained at different points in time. The biologist looks at these values not as individual points, but as a progression over time. Our program (SPOT) helps the researcher find these patterns in large sets of microarray data. A researcher proceeds through three subsequent steps: first, selection of microarray data of interesting experiments from NCBI GEO, second, translating the temporal measurements into time intervals, and third, defining temporal concepts like "peaks" based on those intervals. Then he/she can search for genes that exhibit that particular pattern within the previously selected data pool. We created a software tool using open-source platforms that supports the R statistical package, Bioconductor, and Web 2.0 knowledge representation standards using the open source Semantic Web tool Protégé-OWL. We report here on the web interface that connects to programs based on R and Bioconductor.