Methods for Integrated analysis of Mulptiple Omics datasets : MIMOmics (R-3996)

This project develops robust and efficient statistical methods for the analysis of metabolomics, proteomics, glycomics, and genomic datasets in large studies. Metabolomics and glycomics - in combination with genomics - have great potential for finding predictors for diseases and healthy aging. Proteomics technologies are now ready to be applied to large studies and protein biomarkers will be developed. Methods for omics dataset analysis are typically restricted to single parameter association or single dataset analysis. These methods ignore the complexity of the datasets and the traits. It is expected that multiple markers together will better predict diseases and healthy aging. For example, healthy aging is driven by beneficial combinations of various biological processes: sugar handling, energy storage, immune responses etc. Methods that jointly analyse multiple omics datasets are desperately needed to bring biological research further. The objectives of the project consortium are to develop a statistical framework of methods for all analysis steps needed for identifying and interpreting omics-based biomarkers, and to integrate such data derived from multiple omics platforms across several study designs and populations.

Keywords:glycomics, proteomics, statistical modelling

Disciplines:Applied mathematics in specific fields, Statistics and numerical methods

Project type:Collaboration project