Background The identification and validation of neuroprotective targets is of primary importance to develop Huntington's disease (HD) therapeutics. This inherited neurodegenerative disease is extensively studied thanks to well characterised models that were developed in several species (invertebrates, mammals) and that recapitulate complementary components of HD pathogenesis. Genome wide analyses in these models have generated a large amount of data (dysregulated genes, modifier genes) with high potential for target and marker selection.

Aims The comprehensive and unbiased integration of ‘omics data’ on HD may allow better decisions in candidate target selection to be reached. To this end, the network based analysis of large datasets is anticipated to be highly instructive.

Methods We have designed a network based procedure for integrating data from different models of HD pathogenesis. We aimed at preserving useful information from individual screens and allowing testing for the probabilistic interdependencies of different datasets/variables. The core method is the spectral analysis of the data using large and integrated networks such as WormNet to gradually remove unreliable information.

Results Our procedure extracts gene clusters that are highly interconnected, enriched in HD data and automatically annotated for their biological role and biomedical potential.

Conclusion Preliminary results will be shown to illustrate how our data analysis procedure is able to identify biological processes/pathways/genes of high interest in HD. Further investigation will aim at developing analyses and making the resulting information publically available online.