xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data
Abstract Background Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures.Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance