Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification.

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.

Mass Spectral Feature List Optimizer (MS-FLO): A Tool To Minimize False Positive Peak Reports in Untargeted Liquid Chromatography-Mass Spectroscopy (LC-MS) Data Processing.

Untargeted metabolomics by liquid chromatography-mass spectrometry generates data-rich chromatograms in the form of m/z-retention time features. Managing such datasets is a bottleneck. Many popular data processing tools, including XCMS-online and MZmine2, yield numerous false-positive peak detections. Flagging and removing such false peaks manually is a time-consuming task and prone to human error. We present a web application, Mass Spectral Feature List Optimizer (MS-FLO), to improve the quality of feature lists after initial processing to expedite the process of data curation. The tool utilizes retention time alignments, accurate mass tolerances, Pearson's correlation analysis, and peak height similarity to identify ion adducts, duplicate peak reports, and isotopic features of the main monoisotopic metabolites. Removing such erroneous peaks reduces the overall number of metabolites in data reports and improves the quality of subsequent statistical investigations. To demonstrate the effectiveness of MS-FLO, we processed 28 biological studies and uploaded raw and results data to the Metabolomics Workbench website ( www.metabolomicsworkbench.org ), encompassing 1481 chromatograms produced by two different data processing programs used in-house (MZmine2 and later MS-DIAL). Post-processing of datasets with MS-FLO yielded a 7.8% automated reduction of total peak features and flagged an additional 7.9% of features, per dataset, for review by the user. When manually curated, 87% of these additional flagged features were verified false positives. MS-FLO is an open source web application that is freely available for use at http://msflo.fiehnlab.ucdavis.edu .