DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations....

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2022-04, Vol.94 (16), p.6130-6138
Main Authors: Colby, Sean M, Chang, Christine H, Bade, Jessica L, Nunez, Jamie R, Blumer, Madison R, Orton, Daniel J, Bloodsworth, Kent J, Nakayasu, Ernesto S, Smith, Richard D, Ibrahim, Yehia M, Renslow, Ryan S, Metz, Thomas O
Format: Article
Language:English
Subjects:
Algorithms
Alignment
Analytical chemistry
Calibration
Chemistry
Chromatography, Liquid - methods
Data analysis
Data processing
Deimos
Dimensional analysis
Feature extraction
Instrumentation
Ions
Mass spectra
Mass spectrometry
Mass spectroscopy
Metabolomics
Metabolomics - methods
Programming languages
Scientific imaging
Software
Tandem Mass Spectrometry - methods
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Summary:We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.
ISSN:0003-2700
1520-6882
1520-6882
DOI:10.1021/acs.analchem.1c05017