Unifying the integration, analysis and interpretation of multi-omic datasets: exploration of the disease networks of Obstructive Nephropathy in children.

The wealth of data amassed by the utilization of various high-throughput techniques, in various layers of molecular dissection, stresses the critical role of the unification of the computational methodologies applied in biological data handling, storage, analysis and visualization. In this article, a generic workflow is showcased in a multi-omic dataset that is used to study Obstructive Nephropathy (ON) in children, integrating microarray data from several biological layers (transcriptomic, post-transcriptomic, proteomic). The workflow exploits raw measurements and through several analytical stages (preprocessing, statistical and functional), which entail various parsing steps, reaches the visualization stage of the heterogeneous, broader, molecular interacting network derived. This network, where the interconnected entities are exploiting the knowledge stored in public repositories, represents a systems level interpretation of the pathological state probed.