Exploiting scientific workflows for large-scale gene expression data analysis

Abstract

Microarrays are state technologies of the art for the measurement of expression of thousands of genes in a single experiment. The treatment of these data are typically performed with a wide range of tools, but the understanding of complex biological system by means of gene expression usually requires integrating different types of data from multiple sources and different services and tools. Many efforts are being developed on the new area of scientific workflows in order to create a technology that links both data and tools to create workflows that can easily be used by researchers. Currently technologies in this area aren't mature yet, making arduous the use of these technologies by the researcher. In this paper we present an architecture that helps the researchers to make large-scale gene expression data analysis with cutting edge technologies. The main underlying idea is to automate and rearrange the activities involved in gene expression data analysis, in order to freeing the user of superfluous technological details and tedious and error-prone tasks.