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
