A central aim of differential gene expression profile analysis is to provide an interpretation of given data at the level of biological processes and pathways. However, traversing descriptive data into context is not straightforward. We present a gene-centric dependency graph approach supporting an interpretation of omics profiles at the level of affected networks. The core of our dependency graph comprises data objects encoding the functional categorization of a particular gene, its tissue-specific reference gene expression, as well as known interactions and subcellular location of assigned proteins. On the basis of these genome, transcriptome, and proteome data we compute pair-wise object (gene) dependencies and interpret them as weighted edges in a dependency graph. Mapping of omics profiles on this graph can be used to identify connectors linking features of the omics list, in turn providing the basis for identification of subgraphs and motifs characterizing the cellular state under analysis. We exemplify this approach by analyzing differential gene expression data characterizing B-cell lymphoma and demonstrate the identification of B-cell lymphoma associated subgraphs.