Introduction to LAMP

Untargeted metabolomics studies routinely apply liquid chromatography-mass spectrometry to acquire data for hundreds or low thousands of metabolites and exposome-related (bio)chemicals. The annotation or higher-confidence identification of metabolites and biochemicals can apply multiple different data types (1) chromatographic retention time, (2) the mass-to-charge (m/z) ratio of ions formed during electrospray ionisation for the structurally intact metabolite or (bio)chemical and (3) fragmentation mass spectra derived from MS/MS or MSⁿ experiments.

Commonly, the mass-to-charge (m/z) ratio of ions formed during electrospray ionisation for the structurally intact metabolite are applied as a first step in the annotation process. Importantly, a single metabolite can be detected as multiple different ion types (adducts, isotopes, in-source fragments, oligomers) and grouping together of features representing the same metabolite or biochemical can decrease the number of false positive annotations. The Liverpool Annotation of metabolites using Mass sPectrometry (LAMP) is a Python package and an easy-to-use software for feature grouping and metabolite annotation using MS1 data only. LAMP groups features based on chromatographic retention time similarity and positive response-based correlations across multiple biological samples. Genome-scale metabolic models are the source of metabolites applied in the standard reference files though any source of metabolites can be used (e.g. HMDB or LIPIDMAPS). The m/z differences related to in-source fragments, adducts, isotopes, oligomers and charge states can be user-defined in the reference file.