Journal
MASS SPECTROMETRY REVIEWS
Volume 37, Issue 4, Pages 513-532Publisher
WILEY
DOI: 10.1002/mas.21535
Keywords
compound identification; high-resolution mass spectrometry; library search; tandem mass spectrometry
Categories
Funding
- NSF MCB [1139644, 1153491, 1611846]
- NSF CBET [1438211]
- NSF IOS [1340058]
- NIH [U24 DK097154, 7R01HL091357-06]
- American Heart Association [15SDG25760020]
- Grants-in-Aid for Scientific Research [15H05897, 15K21738, 15H05898] Funding Source: KAKEN
- Direct For Biological Sciences [1611846, 1139644, 1153491] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Division Of Integrative Organismal Systems [1340058] Funding Source: National Science Foundation
- Directorate For Engineering [1438211] Funding Source: National Science Foundation
- Division Of Integrative Organismal Systems [1139644] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys [1438211] Funding Source: National Science Foundation
- Div Of Molecular and Cellular Bioscience [1611846, 1153491] Funding Source: National Science Foundation
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Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.
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