"Omics" Result Interpretation Using Interaction Networks

Gaining insight into phenotypes requires the identification of the underlying processes and pathways that drive the phenotype under research. Wet-lab techniques currently rely heavily on omics experiments to identify these processes. These omics experiments identify gene lists which are commonly analyzed using gene set enrichments. This type of analysis is seriously limited to the available gene sets and does not provide a mechanistic insight into the phenotype (Khatri et al., 2012). Interaction networks, representing the interactome of the organism under study allow to overcome these limitations. These interaction networks allow to select sub-networks that link the genes identified in the omics experiments. These sub-networks represent the actual processes or pathways driving the phenotype. Based on this knowledge we developed PheNetic(De Maeyer et al., 2013), a sub-network selection algorithm that allows interpretation of omics data. We applied PheNetic on a newly compiled interaction network for Escherichia coli to analyze an omics dataset obtained from a study investigating the mechanisms behind acid resistance in E. coli (Stincone et al., 2011). PheNetic allowed the identification of previously described mechanism of acid resistance, confirmed the potential role of OmpR in acid resistance and identified new potential regulators/mechanisms in acid resistance. These results illustrate the potential of the underlying decision theoretic approach used by PheNetic to query interaction networks. Khatri,P. et al. (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS computational biology, 8, e1002375. De Maeyer,D. et al. (2013) PheNetic: network-based interpretation of unstructured gene lists in E. coli. Molecular bioSystems, 9, 1594–603. Stincone,A. et al. (2011) A systems biology approach sheds new light on Escherichia coli acid resistance. Nucleic acids research, 1–17.status: publishe