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Gregory Jordan (EMBL-EBI, Cambridge, United Kingdom)
The extraction of biological insight from the results of genome-wide studies is an increasingly important problem. One common approach is to define a set of “interesting” genes (e.g., genes showing differential expression) and, with reference to a database of structured biological annotations, identify terms or pathways that are over- or under-enriched in the genes of interest. The usual output of such an analysis is a list of the most strongly enriched terms for a given set of interesting genes. The simplicity of such a list of enriched terms belies the many layers of complexity underlying its derivation: the parameters used to determine the set of genes, the source of annotations used, and the method for identifying term enrichment can all have an impact on the results of such analyses. I will present a small number of simple, practical visualizations intended to help better summarize and interpret the results of term enrichment analyses.