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Updated: Feb 3, 2026

Semi-Quantitative Analysis of Peptidoglycan by Liquid Chromatography Mass Spectrometry and Bioinformatics
Published on: October 13, 2020
A Peptide-Level Fully Annotated Data Set for Quantitative Evaluation of Precursor-Aware Mass Spectrometry Data
Jessica Henning1, Annika Tostengard1, Rob Smith1,2
1Department of Computer Science , University of Montana , Missoula , Montana 59812 , United States.
Abstract:
Modern label-free quantitative mass spectrometry workflows are complex experimental chains for devising the composition of biological samples. With benchtop and in silico experimental steps that each have a significant effect on the accuracy, coverage, and statistical significance of the study result, it is crucial to understand the efficacy and biases of each protocol decision. Although many studies have been conducted on wet lab experimental protocols, postacquisition data processing methods have not been adequately evaluated in large part due to a lack of available ground truth data. In this study, we provide a novel ground truth data set for mass spectrometry data analysis at the precursor (MS1) signal level comprised of isolated peptide signals from UPS2, a popular complex standard for proteomics analysis, requiring more than 1000 h of manual curation. The data set consists of more than 62 million points with 1,294,008 grouped into 57,518 extracted ion chromatograms and those grouped into 14,111 isotopic envelopes. This data set can be used to evaluate many aspects of mass spectrometry data processing, including precursor mapping and signal extraction algorithms.
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