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Methods for Integrated analysis of Multiple Omics datasets

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Using big data for metabolic health

An EU research team is finding ways to combine large biological data sets that will shed new light on metabolic diseases like obesity and hypertension.


Metabolic diseases like obesity, diabetes and hyperglycaemia are becoming increasingly common in the modern age. At the same time, huge volumes of biological data are being produced, but not used as effectively as they could be. The EU-funded MIMOMICS (Methods for integrated analysis of multiple omics datasets) project is using this data to better understand metabolic diseases in humans. The project is focusing on so-called omics data – data sets describing the entire genome, proteome or metabolome of an organism. Researchers are producing large volumes of this sort of data, but no one has studied, for example, how the genome and the metabolome are related. This type of information could help scientists to better understand metabolic diseases. MIMOMICS has access to two large data sets, which include genomic, proteomic and metabolomic data, through partner research organisations. Researchers have used these data sets to design methods to analyse combined omics data. Another important part of the project has been harmonising the various data sets so that they can be compared. The software and protocols created as a part of this effort have been made publicly available to the research community. Preliminary work using these data sets has produced methods to predict metabolic health and body composition based on genomic and metabolomic profiles. In the long term, this research will lead to better health predictions from omics data and improved metabolic health.


Metabolic, health, data sets, metabolic diseases, omics, metabolome

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