Linking in silico MS/MS spectra with chemistry data to improve identification of unknowns

Scientific Data
Andrew D McEachranAntony J Williams

Abstract

Confident identification of unknown chemicals in high resolution mass spectrometry (HRMS) screening studies requires cohesive workflows and complementary data, tools, and software. Chemistry databases, screening libraries, and chemical metadata have become fixtures in identification workflows. To increase confidence in compound identifications, the use of structural fragmentation data collected via tandem mass spectrometry (MS/MS or MS2) is vital. However, the availability of empirically collected MS/MS data for identification of unknowns is limited. Researchers have therefore turned to in silico generation of MS/MS data for use in HRMS-based screening studies. This paper describes the generation en masse of predicted MS/MS spectra for the entirety of the US EPA's DSSTox database using competitive fragmentation modelling and a freely available open source tool, CFM-ID. The generated dataset comprises predicted MS/MS spectra for ~700,000 structures, and mappings between predicted spectra, structures, associated substances, and chemical metadata. Together, these resources facilitate improved compound identifications in HRMS screening studies. These data are accessible via an SQL database, a comma-separated export file (.csv), and...Continue Reading

References

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Citations

Jun 27, 2020·Metabolites·Andrew D McEachranAntony J Williams
Mar 4, 2021·Journal of the American Society for Mass Spectrometry·Michael J TaylorChristopher R Anderton
Mar 25, 2021·Briefings in Bioinformatics·Christoph A Krettler, Gerhard G Thallinger
Mar 27, 2021·Frontiers in Public Health·Elliott J PriceJana Klánová
Dec 12, 2019·Analytical Chemistry·Susan D Richardson, Susana Y Kimura

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Software Mentioned

ID SourceForge
MySQL
Dashboard
CompTox Chemicals Dashboard
NTA
PyMySQL
CSI Finger - ID
DSSTox
MetFrag
MS

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