2020BBSRC-NSF/BIO: Linking Mass Spectrometry Computational Ecosystems to Enhance Biological Insights of Publicly-Available Data

All biological systems are composed of important chemicals which help the systems grow, respond to the environment and communicate. The genome is composed of DNA which carries genetic information required for reproduction, growth and development and whose composition remains relatively static throughout a lifetime. The metabolome is composed of metabolites derived from food (e.g. sugars) and the environment (e.g. prescribed drugs) and whose composition is dynamic in relation to which metabolites are present and at what concentration.

The scientific research community studies metabolites to understand how we metabolise food and drugs, how we respond dynamically to the environment and also to identify metabolites which are important for a biological system to function. Many of these investigations are discovery studies which apply a scientific technique called metabolomics that can detect hundreds or low thousands of different metabolites in a biological sample.

These studies discover new metabolites which then have to be analysed to understand their chemical structure which is essential for biological interpretation.

Many metabolomic studies performed across the world are being released to the scientific community so that these data can be re-used and re-analysed to derive new biological information. The data from these studies are stored in data repositories and two examples of these are MetaboLights in the UK and GNPS in the USA. However, many of the metabolites detected and reported in these data repositories do not have a chemical identity assigned to them.

Therefore there is an essential requirement to assign chemical structures to as many metabolites as is possible so that the data can be reused and biological information derived from the data. The planned project will further develop and apply computational approaches to all data deposited in MetaboLights and GNPS. This will allow more metabolites to be assigned a chemical identity and for the confidence that the correct structure has been assigned to be increased.

When completed, the volume and quality of biological information available in the deposited datasets will be much greater and will allow new research questions to be asked and answered without the need to collect new data.