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Computer based strategy for studying correlations between levels of gene expression
Microarray data can be used to derive an understanding of the relationships between the genes involved in various biological systems of an organism, given the availability of web applications and databases of gene expression measurements from the complete spectrum of experimental conditions and materials (Lee et al., 2009). However, there have been no reports, to date, of a web application that can perform comprehensive gene expression correlation analysis. Here, I describe an integrated approach to analyzing gene expression correlation and molecular pathway associated with them. This will also address some commonly asked questions in querying and analyzing gene expression data to facilitate target ID / validation activities. Also, in the future this will help project teams to explore gene expression data for relevant pathways. Furthermore, this approach assists in elucidating relationships between genes and genetic disorders. Until now, researchers used methods that required more effort to gather results and deduce relationships among genes (Janes et al., 2008). I built a web application called Gene-AP to address this problem. Gene-AP can use a "query" gene, along with parameters set by the investigator, from pre- developed microarray gene expression correlation data. The parameters to be considered may include a correlation score and data limit. Gene-AP can also perform statistical analyses of biological relationships between query genes and theoretically connected pathways using functional enrichment analysis using Cytoscape which is integrated in this web application. This utility has been tested on a particular set of data using ESR1 (estrogen receptor gene as the query gene). I found various pathways such as intracellular trafficking and ion binding to be associated with the ESR1 gene products. These results are similar to those found in earlier wet-lab experiments. I intend to improve upon this strategy which will facilitate better interpretation of gene expression correlation data and allow exploration of additional developmental pathways.