Deciphering Gene Sets Annotations with Ontology Based Visualization

Nowadays, one of the main challenges in biology is to make use of several sources of data to improve our understanding of life. When analyzing experimental data, researchers aim at clustering genes that show a similar behavior through specific external conditions. Thus, the functional interpretation of genes is crucial and involves making use of the whole subset of terms that annotate these genes and which can be relatively large and redundant. The manual expertise to clearly decipher the main functions that may be related to the gene set is timeconsuming and becomes impracticable when the number of gene sets increases, like in the case of vaccine/drug trials. To overcome this drawback, it may be necessary to reduce the dataset with the aim to apply visualization approaches. In this paper, we propose a new pipeline combining enrichment and annotation terms simplification to produce a synthetic visualization of several gene sets simultaneously. We illustrate the efficiency of our method on a case study aiming at analyzing the immune response in diseases.