Abstract
Introduction : Untargeted LC-MS metabolomics detects thousands of signals in each experiment. Some of them refer to real measured metabolites, but others, known as non-biological signals, can be associated to a wide range of factors, such as background ions, chemical contaminants or informatic artefacts, among others. It is broadly recognized that one of the major bottlenecks in the metabolomics workflow is metabolite identification, which is time-consuming and usually has a considerable failure rate.
Technological and methodological innovation : A large number of metabolites are detected recurrently in different studies on the same sample type when using the same analytical method. Knowledge acquired in previous experiments thus allows to accelerate the identification process of new studies and enables to focus on novel and/or still unknown metabolites. We propose to implement functionality required to annotate signals in a specific system and to build a reference database facilitating annotations of subsequent studies.
Results and impact : Currently we are listing signals associated with specific compounds, but also yet unidentified ions detected in the system. We think that in the future we will be able to provide a catalogue of metabolites detected with a specific analytical method in a specific biological matrix. The effectiveness of this pipeline is now evaluated using a series of different standard mixtures and next we plan to apply this on complex biological matrices.