![]() These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. Metabolomics have proven difficult to execute in an untargeted and generalizable manner. We also find improvements of matching spectra across instrument types (between an Agilent Q-TOF and an Orbitrap Elite), at high collision energies (50 - 90 eV), and with smaller datasets available through MassBank. Applying this method yields significant improvements (~10% more spectra correctly identified) on large datasets (2,000 - 10,000 spectra), indicating this is a quick yet adept tool for improving spectral matching in situations where available reference libraries are not yet sufficient. Supplementing existing spectral databases with interpolated spectra yields consistent improvements to identification accuracy on a range of instruments and precursor types. We find that highly accurate spectral approximations can be interpolated from as few as three experimental spectra and that the interpolated more » spectra will be consistent with true spectra gathered from the same instrument as the experimental spectra. Here we present a method to improve the usefulness of existing MS/MS libraries by augmenting available experimental spectra datasets with statistically interpolated spectra at unreported collision energies. The high degree of variability in MS/MS spectrum acquisition techniques and parameters creates a significant challenge for building standardized reference libraries. Tandem mass spectrometry (MS/MS) is a primary tool for identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. We also analyzed statistics on adducts from spectra contained in the three selected mass spectral libraries. While the included adduct database focuses on adducts typically detected during liquid chromatography-mass spectrometry analyses, users may supply their own lists of adducts and charge states for calculating expected m/z. The calculator relies on user-selected subsets of the combined database to calculate expected m/z for adducts of molecules supplied as formulas This tool is intended to help researchers create identification libraries to collect evidence for the presence of molecules in mass spectrometry data. MSAC includes a database of 2,341 potential ions and their mass-to-charge ratios (m/z) as extracted from the NIST/EPA/NIH Mass Spectral Library (NIST17), the Global Natural Products Social Molecular Networking Public Spectral Libraries (GNPS), and MassBank of North America (MoNA). Here, adduct refers to a version of a parent molecule that is charged due to addition or loss of atoms and electrons resulting in a charged ion, e.g. We describe the Mass Spectrometry Adduct Calculator (MSAC), an automated Python tool to calculate the adduct ion masses of a parent molecule.
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