Large scale analysis of OMICS data for drug-target finding in neurodegenerative diseases - ERA-NET JPcofuND 2 - 2023

In the context of JPcofuND 2 ANR launches a joint transnational call; the aim is to establish a number of ambitious innovative, multi-disciplinary and multi-national collaborative research projects that seek to improve the understanding of the complex and multifactorial pathogenesis of neurodegenerative diseases by applying multi-OMICs and Big data approaches in order to generate useful information for the diagnosis, treatment, prognosis, and drug discovery of neurodegenerative diseases.

The research approaches should be integrative, combining relevant scientific approaches (e.g. clinical, epidemiological, molecular, experimental) and involve state-of-the art methodology and techniques. Proposals must be hypothesis-driven and should have a strong emphasis on reliable methodology. They should be based on mechanistic approaches rather than pure correlational research. The potential of existing cohorts and data sets should be exploited. Projects should ideally aim at applying state-of-the-art techniques using existing biomaterials, and complementing existing data sets. Proposals submitted under this call may include, but are not limited to, the following types of research:

• Using the potential from existing animal and cellular models and cohorts, including available data and biomaterial, for conducting large-scale OMICS approaches to unravel the interplay and interactions of molecules from multiple molecular levels driving disease pathogenesis.
   
• Better understanding the complex and multi-factorial mechanisms of disease onset and progression as well as the corresponding influencing factors.
   
• Translating the findings from Big Data analysis and multi-OMICS approaches to existing animal and cellular models, thereby enhancing the potential of these models.
   
• Identification of new drug targets or novel starting points for pharmacological interventions and prevention.
   
• Sharpening the current understanding of disease definition, thus leading to enhanced sub-classification and better patient stratification.
   
• Identification of novel and validation of already established biomarkers.