Analyzing and Visualizing Genomic Complexity for the Derivation of the Emergent Molecular Networks

Modern genomic studies, accumulation of biological information in repositories, plus novel analytical and data-mining methodologies, comprise the backbone for the holistic explanation of intricate phenotypes, interrogated by high-throughput experiments. Recent developments in web platforms architecture, in conjunction with novel, browser-centric, visualization techniques pose a powerful framework for the development of distributed web applications, which execute complex analytical tasks, display the results in user-friendly interface and produce comprehensive, visualization charts. In this paper, the presented client-server application targets the systemic interpretation of input gene lists, through the fusion of established statistical methodologies and information-mining techniques, while interactive visualization modules aid the intuitive interpretation of results. Two publicly available datasets, related to Crohn's and Parkinson's disease are used to present application analytical efficiency, robustness and functionalities.