Scientists use gene expression profile analysis to explore the genetic basis of complex diseases, such as cancer. The output of many expression studies is a set of potential candidates which needs qualification by literature research.
I2E is used to search comprehensively across the literature for genotype-phenotype associations that other tools and search strategies will struggle to locate.
Gene expression profile analysis is the study of the way in which genes are transcribed to produce functional gene products (functional RNA species or protein products). The study of gene expression provides insights into regulation of normal cellular processes, such as cell differentiation, or abnormal or pathological processes.
Expression profiles can also look at responses of sets of genes (sometimes the entire genome) to a particular treatment or toxin, for efficacy or safety modelling.
There has been tremendous innovation in gene expression technologies, including high-throughput assays such as microarrays, and sequence-based techniques such as SAGE and RNA-Seq. Visualizations (e.g. heat maps) and statistical analysis (e.g. clustering) can pull out the gene products that change under particular conditions or in particular disease states. But making biological sense of the gene expression profiles relies on annotation around the gene function, tissue, cellular or chromosomal location, etc., which I2E can provide.
miRNA expression profiling and validation
I2E has been used to add value to the results of miRNA expression profiling in animal models of disease. See:
von Schack et al (2011); “Dynamic changes in the microRNA expression profile reveal multiple regulatory mechanisms in the spinal nerve ligation model of neuropathic pain.” PLoS One. 2011 Mar 14;6(3):e17670;
Murray BS et al (2010): “An in silico analysis of microRNAs: mining the miRNAome.” Mol Biosyst. 2010 Oct;6(10):1853-62