Multidisciplinary Collaboration to Facilitate Hypotheses Generation in Huntington's Disease

Biomedical research has become more computational and approaches now typically rely on the collaboration between laboratory researchers and bioinformaticians. Large biomedical datasets already exist and that creates the need for efficient data analysis that will facilitate the process of generating new insights for developing treatments for rare diseases such as Huntington's Disease. In this paper we demonstrate our approach of investigating the epigenetic mechanisms that are involved in Huntington's disease, using a workflow centric e-Science approach. Our results rely on a collaborative schema that loops over design and execution of computational workflows while maintaining close collaboration with experts of Huntington's Disease and epigenetics, to discuss new biological insights and follow-up experiments (both computational and wet-lab). Our computational tools further support the collaboration by providing suggestions about mechanisms and genes that may be implicated in the disease and that can serve as hypotheses for further investigation. We publish our results and methods using the nanopublication and Research Object model respectively, for future availability and preservation.