Interpretation requires context – making sense out of gene lists and networks

Abstract
Many functional genomics and systems biology approaches result in lists of genes with common properties. The interpretation of a list of genes is crucial in understanding the results of such an experiment. Typical approaches are to study gene enrichment of pathway or GO categories, such as to find out which biological processes are most affected in a given experiment.
Our work has focused on interpreting genes and gene lists by bringing them into the context of a wide variety of experimental conditions. For example, finding out in which tissues, at what time, and in response to which factors a gene is expressed helps understanding the function of this gene and its potential role in one’s own experiment. To achieve this, we manually curated, annotated, quality controlled and normalized more than 55’000 expression microarrays from thousands of public experiments, and created user-friendly tools to visualize gene expression across these contexts. Several such meta-analysis tools are freely available at GeneVestigator .