Integrated approach for metabolite identification in LC-MS

Annotation of peaks and rapid identification of metabolites is currently still a major bottle-neck in mass spectrometry (MS) based metabolomics. Despite numerous new algorithms and software packages published in recent years, including attempts at de-novo identification and modeling, the workhorse of the trade remains mass-to-mass and retention time (RT) matching of observed and reference library peaks, including, when available, MS/MS data which is used for confirmation. In this work, we present a rational, statistically based expansion of the used approach using orthogonal computational modules and accumulation of independent evidence to achieve automatic high confidence identifications of metabolites in LC-MS data.