Open AccessDEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data
Author(s) -
Sean Colby,
Christine Chang,
Jessica Bade,
Jamie Nuñez,
Madison Blumer,
Daniel J. Orton,
Kent Bloodsworth,
Ernesto Nakayasu,
Richard Smith,
Yehia Ibrahim,
Ryan Renslow,
Thomas O. Metz
Publication year - 2022
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.1c05017
Subject(s) - deconvolution , computer science , mass spectrometry , chemistry , data processing , algorithm , database , chromatography
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.