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DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics

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Tsou, Chih-Chiang, Avtonomov, Dmitry, Larsen, Brett, Tucholska, Monika, Choi, Hyungwon, Gingras, Anne-Claude, Nesvizhskii, Alexey I·2015·Nature Methods
DOI 10.1038/nmeth.3255
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Abstract

As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo–tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification–MS data.

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Metadata

Authors
Tsou, C.-C., Avtonomov, D., Larsen, B., Tucholska, M., Choi, H., Gingras, A.-C., & Nesvizhskii, A. I.
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2015-01-19
Volume
12
Issue
3
Pages
258-264
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/nmeth.3255
OpenAlex
W2045117578
Cited by
644
References
9
FWCI
24.90
Type
journal article
Language
en
Open access
green
Licence
www.springer.com/tdm (tdm)

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CoChat. (2026). Citation check: DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. https://app.cochat.ai/papers/10.1038/nmeth.3255
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Tsou, C.-C., Avtonomov, D., Larsen, B., Tucholska, M., Choi, H., Gingras, A.-C., & Nesvizhskii, A. I. (2015). DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nature Methods. https://doi.org/10.1038/nmeth.3255

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