Nat Methods. 2021 Dec;18(12):1524-1531. doi: 10.1038/s41592-021-01331-z. | PubMed

Yuanyue Li¹, Tobias Kind¹, Jacob Folz¹, Arpana Vaniya¹, Sajjan Singh Mehta^1,2, Oliver Fiehn³

1. West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA.
2. Olobion, Parc Científic de Barcelona, Barcelona, Spain.
3. West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA. ofiehn@ucdavis.edu.

Abstract

Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.

Presented By Yuanyue Li | ORCID iD