Zhiwei Zhou

TL;DR: A comprehensive lipidome atlas with retention time, collision cross-section and tandem mass spectrometry information is presented and mass spectral fragmentations of lipids across 117 lipid subclasses are presented in a lipidomeAtlas.

TL;DR: AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples, revealing comprehensive chemical and metabolic insights towards biological processes.

TL;DR: This work demonstrated the use of a machine-learning algorithm called support vector regression (SVR) to develop a prediction method that utilized 14 common molecular descriptors to predict CCS values for metabolites and proved that the SVR based prediction method can accurately predict nitrogen CCSvalues (ΩN2) of metabolites from molecular descriptor and effectively improve identification accuracy and efficiency in untargeted metabolomics.

TL;DR: In LipidCCS, a set of molecular descriptors were optimized using bioinformatic approaches to comprehensively describe the subtle structure differences for lipids and the improved precision could effectively reduce false-positive identifications of lipids.

TL;DR: This work developed the first web server, namely, MetCCS Predictor, which can predict the CCS values of metabolites using molecular descriptors within a few seconds, and effectively improve the metabolite identification in metabolomics.