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

Colby, Sean M; Chang, Christine H; Bade, Jessica L; Nunez, Jamie; Blumer, Madison R; Orton, Daniel J; Bloodsworth, Kent J; Nakayasu, Ernesto S; Smith, Richard D; Ibrahim, Yehia M; Renslow, Ryan S; Metz, Thomas O

doi:10.25584/2483273

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

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Abstract

We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface and command-line tool for high-dimensional mass spectrometry data analysis workflows, offering ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section 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 utilize all dimensions simultaneously to (i) offer greater separation between features, improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data, demonstrating the advantages of a multidimensional approach in each data processing step.

Authors:

Colby, Sean M; Chang, Christine H;

; Nunez, Jamie; Blumer, Madison R;

;

PNNL

Publication Date:: Tue Nov 16 04:00:00 UTC 2021

DOE Contract Number:: AC05-76RL01830

Research Org.:: Pacific Northwest National Laboratory 2

Sponsoring Org.:: DOE

OSTI Identifier:: 2483273

DOI:: https://doi.org/10.25584/2483273

Citation Formats


                    Colby, Sean M, Chang, Christine H, Bade, Jessica L, Nunez, Jamie, Blumer, Madison R, Orton, Daniel J, Bloodsworth, Kent J, Nakayasu, Ernesto S, Smith, Richard D, Ibrahim, Yehia M, Renslow, Ryan S, and Metz, Thomas O. DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.  United States: N. p., 2021. 
        Web.  doi:10.25584/2483273.

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                    Colby, Sean M, Chang, Christine H, Bade, Jessica L, Nunez, Jamie, Blumer, Madison R, Orton, Daniel J, Bloodsworth, Kent J, Nakayasu, Ernesto S, Smith, Richard D, Ibrahim, Yehia M, Renslow, Ryan S, & Metz, Thomas O. DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.  United States.  doi:https://doi.org/10.25584/2483273

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                    Colby, Sean M, Chang, Christine H, Bade, Jessica L, Nunez, Jamie, Blumer, Madison R, Orton, Daniel J, Bloodsworth, Kent J, Nakayasu, Ernesto S, Smith, Richard D, Ibrahim, Yehia M, Renslow, Ryan S, and Metz, Thomas O. 2021.  
"DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data".  United States.  doi:https://doi.org/10.25584/2483273.  https://www.osti.gov/servlets/purl/2483273. Pub date:Tue Nov 16 04:00:00 UTC 2021

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@article{osti_2483273,

  title        = {DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data},

  author       = {Colby, Sean M and Chang, Christine H and Bade, Jessica L and Nunez, Jamie and Blumer, Madison R and Orton, Daniel J and Bloodsworth, Kent J and Nakayasu, Ernesto S and Smith, Richard D and Ibrahim, Yehia M and Renslow, Ryan S and Metz, Thomas O},

  abstractNote = {We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface and command-line tool for high-dimensional mass spectrometry data analysis workflows, offering ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section 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 utilize all dimensions simultaneously to (i) offer greater separation between features, improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data, demonstrating the advantages of a multidimensional approach in each data processing step.},

  doi          = {10.25584/2483273},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Nov 16 04:00:00 UTC 2021},

  month        = {Tue Nov 16 04:00:00 UTC 2021}

}

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DOI: https://doi.org/10.25584/2483273

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