Making sense of the biological complexity through the platform-driven unification of the analytical and visualization tasks

The development of several biomedical ontologies and databases for structuring and categorizing knowledge in life sciences, and particularly the ones which refer to the functions and interactions of biomolecules, have contributed to the rapid inflation of the semantic information universe that describes cellular complexity, at different scales. Together with the ever-growing number of high-throughput molecular data, generated by DNA microarray or NGS experiments, they stress the need for powerful, intuitive data representation methods, which manage to make sense out of the myriads of interactions and pinpoint those with a causal contribution to the phenotypes studied. In this paper, we present a web application, which overall combines computational methodologies and data visualization techniques, in order to deliver comprehensible illustrations of cellular complexity, for voluminous, molecular datasets, linking the individual genes, with the relevant biological processes, in which they participate, while it manages to prioritize those processes according to their involvement in the cellular phenotype studied. The application highlights molecular information (functions, processes, cellular compartments) according several criteria (enrichment score, expression, etc) sorts out regulatory hub genes, with a pivotal role in the phenotype studied, while, most importantly, novel visualization modules provide an efficient, intuitive illustration that aids easy systems' level interpretation. The pipeline is showcased here using a colon cancer dataset.