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

Nature Methods volume 12, pages 258–264 (2015)Cite this article

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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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Figure 1: Untargeted and targeted data analysis strategies and DIA-Umpire hybrid framework.
Figure 2: DIA-Umpire signal-processing algorithms.
Figure 3: Untargeted peptide and protein identification with DDA and DIA data from UPS2, E. coli and human cell lysate samples.
Figure 4: Comparative analysis of peptide identifications from DDA and DIA data from human cell lysate samples.
Figure 5: Application of the entire DIA-Umpire workflow to an AP-SWATH interactome data set.

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Acknowledgements

We thank B. MacLean for help with Skyline, H. Röst and B. Collins for help with OpenSWATH and S. Tate for useful discussions. We also thank S. Danielson at Thermo Scientific for access to the Q Exactive Plus and Z.-Y. Lin for the acquisition of the DIA samples for MEPCE, EIF4A2 and GFP. This work was supported by US National Institutes of Health grants 5R01GM94231 (to A.I.N. and A.-C.G.), R01GM107148 and U24DK097153 (to A.I.N.); the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-069 to A.-C.G., A.I.N. and H.C.) and the Canadian Institutes of Health Research (MOP-84314 and MOP-123322 to A.-C.G.); and Singapore Ministry of Education grant R-608-000-088-112 (to H.C.).

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Authors and Affiliations

  1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA

    Chih-Chiang Tsou & Alexey I Nesvizhskii

  2. Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA

    Chih-Chiang Tsou, Dmitry Avtonomov & Alexey I Nesvizhskii

  3. Lunenfeld-Tanenbaum Research Institute, Toronto, Ontario, Canada

    Brett Larsen, Monika Tucholska & Anne-Claude Gingras

  4. Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

    Hyungwon Choi

  5. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Anne-Claude Gingras

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  1. Chih-Chiang Tsou
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Contributions

A.I.N. and C.-C.T. conceived the project and developed the algorithm. C.-C.T. implemented the software. D.A. assisted with the OpenSWATH analysis and contributed to the algorithm and software development. B.L. and M.T. acquired mass spectrometry data. H.C. assisted with SAINT scoring and contributed to the development of protein quantification strategies. A.-C.G., C.-C.T., B.L. and A.I.N. designed experiments and analyzed data. A.I.N. and A.-C.G. supervised the project. C.-C.T., A.I.N. and A.-C.G. wrote the manuscript with input from B.L. and D.A.

Corresponding authors

Correspondence to Anne-Claude Gingras or Alexey I Nesvizhskii.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–29 (PDF 7641 kb)

Supplementary Table 1

Number of protein and peptide ion identifications from DDA MS/MS and DIA pseudo-MS/MS spectra obtained with three different search engines (X!Tandem, Comet and MSGF+), as well as with all three search engines combined (XLSX 12 kb)

Supplementary Table 2

Protein identification and quantification results (protein, peptide ion and fragment ion levels), human cell lysate samples (XLSX 30210 kb)

Supplementary Table 3

Protein identification and quantification results (protein, peptide ion and fragment ion levels), E. coli cell lysate samples (XLSX 20544 kb)

Supplementary Table 4

Protein identification and quantification results (protein, peptide ion and fragment ion levels), UPS2 samples (XLSX 3843 kb)

Supplementary Table 5

Peptide ion identifications from DDA, DIA with DIA-Umpire and DIA with OpenSWATH, human cell lysate samples (XLSX 2051 kb)

Supplementary Table 6

Peptide ion identifications from DDA, DIA with DIA-Umpire and DIA with OpenSWATH, E. coli cell lysate samples (XLSX 1394 kb)

Supplementary Table 7

Protein identification and quantification results (protein, peptide ion and fragment ion levels), DIA (SWATH) glycoproteomics data set (XLSX 19282 kb)

Supplementary Table 8

Protein identification and quantification results (protein, peptide ion and fragment ion levels), AP-SWATH interactome data set (XLSX 42939 kb)

Supplementary Table 9

SAINT results, AP-SWATH interactome data set (XLSX 119 kb)

Supplementary Table 10

Number of peptide ions and proteins for a representative DIA (SWATH) run identified using different thresholds for precursor-fragment grouping (XLSX 12 kb)

Supplementary Table 11

List of the raw mass spectrometry files (XLSX 12 kb)

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Tsou, CC., Avtonomov, D., Larsen, B. et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 12, 258–264 (2015). https://doi.org/10.1038/nmeth.3255

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