Background The generation and large-scale molecular analysis of multiple models of Huntington's disease (HD) and the integration of genome-scale information across species provide a framework with strong potential for best understanding HD biology and prioritising candidate targets. However, on a global scale level, we know little about the variation in pathophysiological patterns across the species in which HD modelling is performed and how this may impact on the target prioritisation and validation process. An important issue in this regard is about the biological significance that may be associated with specific combinations of HD models across species, and the type of HD mechanism/target documented.

Aims To provide a solution for decision-making in HD research, we developed the Biogemix procedure, a comprehensive cross-species network-based method for data integration, model sensitivity analysis and target prioritisation.

Methods The Biogemix procedure is data-driven and unbiased, and it takes advantage of the information contained in probabilistic functional gene networks for pattern discovery in HD. The Biogemix tool chain comprises several features for the optimal use of biologically- and disease-relevant information in large bionetworks. The resulting knowledge is stored into the Biogemix-HD knowledge base prototype, allowing the users to select specific data integration schemes and visualise the results of their queries in a comprehensive fashion thanks to a rich variety of biological- and HD-relevant annotations.

Results and Conclusions Our results highlight similarities and differences between model species in which HD modelling is performed, providing guidance for the selection of individual models/combinations-of-models to prosecute specific targets. We will show the latest developments obtained with the Biogemix-HD knowledge base project and discuss their implications for target validation in HD.