pyQms enables universal and accurate quantification of mass spectrometry data

Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present py...

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Published inMolecular & cellular proteomics Vol. 16; no. 10; pp. 1736 - 1745
Main Authors Leufken, Johannes, Niehues, Anna, Sarin, L. Peter, Wessel, Florian, Hippler, Michael, Leidel, Sebastian A., Fufezan, Christian
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.10.2017
American Society for Biochemistry and Molecular Biology
The American Society for Biochemistry and Molecular Biology
Subjects
Algorithms
Chromatography, Liquid
Glycomics
Ions
Isotope Labeling - methods
Labeling
Mass spectrometry
Mass spectroscopy
Metabolomics
Open source software
Pattern matching
Proteome - analysis
Proteomics
Proteomics - methods
Quality assessment
Quality control
Scientific imaging
Software
Source code
Spectrometry, Mass, Electrospray Ionization - methods
Spectroscopy
Tandem Mass Spectrometry - methods
Technological Innovation and Resources
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Summary:Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present pyQms, an open-source software for accurate quantification of all types of molecules measurable by MS. pyQms uses isotope pattern matching that offers an accurate quality assessment of all quantifications and the ability to directly incorporate mass spectrometer accuracy. pyQms is, due to its universal design, applicable to every research field, labeling strategy, and acquisition technique. This opens ultimate flexibility for researchers to design experiments employing innovative and hitherto unexplored labeling strategies. Importantly, pyQms performs very well to accurately quantify partially labeled proteomes in large scale and high throughput, the most challenging task for a quantification algorithm.
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ISSN:1535-9476
1535-9484
DOI:10.1074/mcp.M117.068007