Olvi Tole ACF Abstract FY11

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.

Page last modified April 29, 2011