Standardised metabolite annotation workflows for enhanced biological interpretation in metabolomic data repositories

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Metabolites are small biochemicals which have many important roles in biological systems including metabolism. Metabolites are studied in many different biological systems including microbes, plants and humans to benefit the human population through increased crop yields, the manufacture of drugs and in understanding how humans age and how the process can be modified to improve our health. The study of metabolites in biological systems is called metabolomics which has the aim to study thousands of metabolites and investigate the biological processes they are involved in. These studies do not know which metabolites will be detected at the start of the study and instead translate the raw analytical data to more biologically meaningful data during the study by chemically identifying the chemical structure of each metabolite, for example to define that the metabolite is glucose or a lipid. The conversion of the data to a metabolite is required so to derive biological conclusions, no metabolite identification will result in no biological information being reported. Many metabolomic studies are made available to all of the scientific community in data repositories. One data repository is located in the UK and is called MetaboLights and the other large data repository is located in the USA and is called the Metabolomics Workbench. These two data repositories contain information from nearly 2000 metabolomic studies performed on microbes, plants and mammals including humans. Across all studies up to 89% of all detected metabolites are not identified, have no chemical structure assigned to them and so significant levels of information from which biological knowledge can not be derived are present. The project to be performed will construct a computational workflow to assign chemical structures to the majority of metabolites in datasets already present in both of these data repositories and also to be applied to all future datasets deposited to the repositories. On completion, the biological information available in the two data repositories will be greatly expanded and will allow further biological information to be derived.