Washington University in St. Louis

The Patti Lab
Metabolomics to elucidate novel biochemical mechanisms of disease
Job Opportunities
Job opportunities are available.

Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The mz.unity Algorithm

Mahieu NG, Spalding J, Gelman S, Patti GJ
Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The mz.unity Algorithm
Anal. Chem., 88(18), 9037-9046, 2016
doi:10.1021/acs.analchem.6b01702

Analysis of a single analyte by mass spectrometry can result in the detection of more than 100 degenerate peaks. These degenerate peaks complicate spectral interpretation and are challenging to annotate. In mass spectrometry-based metabolomics, this degeneracy leads to inflated false discovery rates, data sets containing an order of magnitude more features than analytes, and an inefficient use of resources during data analysis. Although software has been introduced to annotate spectral degeneracy, current approaches are unable to represent several important classes of peak relationships. These include heterodimers and higher complex adducts, distal fragments, relationships between peaks in different polarities, and complex adducts between features and background peaks. Here we outline sources of peak degeneracy in mass spectra that are not annotated by current approaches and introduce a software package called mz.unity to detect these relationships in accurate mass data. Using mz.unity, we find that data sets contain many more complex relationships than we anticipated. Examples include the adduct of glutamate and nicotinamide adenine dinucleotide (NAD), fragments of NAD detected in the same or opposite polarities, and the adduct of glutamate and a background peak. Further, the complex relationships we identify show that several assumptions commonly made when interpreting mass spectral degeneracy do not hold in general. These contributions provide new tools and insight to aid in the annotation of complex spectral relationships and provide a foundation for improved data set identification. Mz.unity is an R package and is freely available at https://github.com/nathaniel-mahieu/mz.unity as well as our laboratory Web site http://pattilab.wustl.edu/software/.

Washington University, Departments of Chemistry, Genetics, and Medicine. Saint Louis, Missouri 63110 USA