A reproducible database for untargeted metabolomics data processing across laboratories

Project Summary Scientific teams led by Prof. Oliver Fiehn (UC Davis), Prof. Charles Evans (University of Michigan) and Dr. Tom Metz (Pacific Northwest National Laboratory) will develop and validate a unified, cloud-based data processing workflow and database for high resolution LC-MS/MS metabolomics and lipidomics data. This new

database, LC-BinBase, will serve as cornerstone to harmonize and standardize metabolomics data reports. Through both experimental and computational robustness tests, we will show that different metabolomics laboratories can generate consensus data sets if the same set of data acquisition methods are used, even if

instruments from different vendors are used. This work will lead the way for next-generation metabolomics, towards faster, more reproducible and robust data processing that leads to standardized data reports and improved interpretability for biomedical researchers. To this end, we will develop algorithms to apply LC-BinBase with calculated metabolite annotation

confidence scores for both hydrophilic interaction- and reversed phase chromatography, and for both QTOF and orbital ion trap high resolution mass spectrometers. These confidence scores will be calculated from deviations of probability distributions of experimental and predicted retention times, accurate masses, MS/MS

entropy similarity scores and ion mobility data. We will validate the usability of LC-BinBase reports for biomedical researchers by interlaboratory comparison studies on diverse sets of mouse organs, plus different cell types and biofluids. Data usability will be improved by a unified compound metadata list for all annotated

metabolites. We will organize events and workshops to invite the scientific community to beta-test LC-BinBase.