PT - JOURNAL ARTICLE AU - Drew R. Jones AU - Xusheng Wang AU - Tim Shaw AU - Ji-Hoon Cho AU - Ping-Chung Chen AU - Kaushik Kumar Dey AU - Suiping Zhou AU - Yuxin Li AU - Nam Chul Kim AU - J. Paul Taylor AU - Udhghatri Kolli AU - Jiaxu Li AU - Junmin Peng TI - Metabolome Identification by Systematic Stable Isotope Labeling Experiments and False Discovery Analysis with a Target-Decoy Strategy AID - 10.1101/089904 DP - 2016 Jan 01 TA - bioRxiv PG - 089904 4099 - http://biorxiv.org/content/early/2016/11/26/089904.short 4100 - http://biorxiv.org/content/early/2016/11/26/089904.full AB - We introduce a formula-based strategy and algorithm (JUMPm) for global metabolite identification and false discovery analysis in untargeted mass spectrometry-based metabolomics. JUMPm determines the chemical formulas of metabolites from unlabeled and stable-isotope labeled metabolome data, and derives the most likely metabolite identity by searching structure databases. JUMPm also estimates the false discovery rate (FDR) with a target-decoy strategy based on the octet rule of chemistry. With systematic stable isotope labeling of yeast, we identified 2,085 chemical formulas (10% FDR), 892 of which were assigned with metabolite structures. We evaluated JUMPm with a library of synthetic standards, and found that 96% of the formulas were correctly identified. We extended the method to mammalian cells with direct isotope labeling and by heavy yeast spike-in. This strategy and algorithm provide a powerful a practical solution for global identification of metabolites with a critical measure of confidence.