MetAnalyzer: Automated Untargeted Metabolomics Data Analysis with Experimental MS/MS Spectral Library
Brief introduction: Untargeted metabolomics has the potential to implicate unexpected pathways with a unique phenotype or disease process. However, the major bottleneck of untargeted metabolomics has been the challenge of determining the identities of the peaks found to be dysregulated in the untargeted profiling data. Currently, identifying metabolites in LC-MS based metabolomics experiments solely relies on high resolution MS data. The problem with this approach is that there may be several molecular formulas that are appropriate for the accurate mass data (depending on the resolution of the instrument), and numerous potential isomers for each molecular formula.Here, we demonstrated to utilize high resolution precursor ion (MS) and tandem mass spectral (MS/MS) fragmentation data to identify metabolites. Integral to this approach is high resolution precursor data, which narrows down potential molecular formulas, and high resolution fragmentation data, which allows for accurate classification by identification of neutral losses and fragment ions. Matching the accurate mass and fragmentation data with standard MS/MS spectra in database provides a more convincing identification than accurate mass alone.In Dr. Zhu lab, we develop MetAnalyzer program tointegrate the metabolite quantitative analysis and structure elucidate all together as an automated untargeted metabolomics data analysis workflow. The metabolite MS/MS spectral library is in-house ZhuMetLib spectral library, now containing standard spectra for ~1500 metabolites.
Availability of MetAnalyzer: Not available for public use before publication.
ZhuMetLib: An In-house, Standard MS/MS Spectral Library for Untargeted Metabolomics