The sequencing of the human genome has identified a vast number of potentially interesting targets for drug development. The challenge is to evaluate these targets and select those that are relevant to human disease. An important step for the evaluation process is to identify where these targets are expressed, and to link this expression to potential therapeutic uses. This evaluation is greatly aided by the provision of high-quality gene-expression data and sophisticated software tools for identifying targets with interesting expression patterns.
In this article, we describe the combination of Asterand’s XpressWay® human gene-expression data with BioWisdom’s visual analytics software, OmniViz. The XpressWay dataset consists of more than 2,000 gene-expression profiles, the majority representing proteins that are potentially tractable drug targets. The OmniViz software provides easily interpreted visualizations and a suite of interactive tools that allows scientists to relate divergent data to uncover previously unknown associations and answer key scientific questions.
By analyzing XpressWay data using OmniViz, the user can quickly select and stratify genes of interest, providing a range of benefits including: increasing confidence in a therapeutic approach, selecting the best targets for drug development, identifying new targets for an indication, exploiting opportunities to switch indications, identifying potential side effect liabilities, predicting translation of preclinical to clinical drug effects, and gaining mechanistic insight by looking at the association of gene-expression profiles.
A Whole-Body Scan
XpressWay profiles consist of target expression in 72 different human tissues from three different donors (216 samples in total). These tissues are chosen to be representative of the major organ systems of the body, and comprise many sub-dissected regions. All the tissues are assessed as pathologically normal by pathologists. The breadth of tissues used for XpressWay profiles holds advantages over other gene-expression datasets that often consist of smaller sets of tissue.
Total RNA is isolated from the tissues using standard methodologies, and has to pass several QC criteria before being considered suitable for expression profiling.