ISGE: A Conceptual Model-Based Method to Correctly Manage Genome Data

Abstract

Understanding the Human Genome is one of the most relevant challenges under current investigation. The ongoing genomics revolution promises to change the diagnosis, treatment, and prevention of disease, providing long-term benefits and a transformative impact on personal health. It also has wealth and productivity implications. However, genomics is one of the most complex and data-intensive domains. Fuzzy definitions, data diversity, data heterogeneity, and continuous evolution of knowledge are responsible for an inadequate and inaccurate understanding of the domain, which hinders the unleashing of its full potential. A sound conceptual modeling-practice is essential in achieving the required shared understanding of the domain. This paper presents ISGE, which is a conceptual model-based method to improve genomic data management from two perspectives: first, by performing a sound, new characterization of genomic data; and second, by providing a framework that encourages applying CM techniques and reusing their generated artifacts in order to take advantage of all of the previously accumulated knowledge. Better abstraction capabilities and efficient reuse are intended to facilitate the work of domain experts.