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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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  1. Nesvizhskii, A.I. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J. Proteomics 73, 2092–2123 (2010). · DOI · Paper page
  2. Bailey, D.J., McDevitt, M.T., Westphall, M.S., Pagliarini, D.J. & Coon, J.J. Intelligent data acquisition blends targeted and discovery methods. J. Proteome Res. 13, 2152–2161 (2014). · DOI
  3. Weisbrod, C.R., Eng, J.K., Hoopmann, M.R., Baker, T. & Bruce, J.E. Accurate peptide fragment mass analysis: multiplexed peptide identification and quantification. J. Proteome Res. 11, 1621–1632 (2012). · DOI
  4. Tate, S., Larsen, B., Bonner, R. & Gingras, A.C. Label-free quantitative proteomics trends for protein-protein interactions. J. Proteomics 81, 91–101 (2013). · DOI
  5. Venable, J.D., Dong, M.Q., Wohlschlegel, J., Dillin, A. & Yates, J.R. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat. Methods 1, 39–45 (2004). · DOI · Paper page
  6. Panchaud, A. et al. Precursor acquisition independent from ion count: how to dive deeper into the proteomics ocean. Anal. Chem. 81, 6481–6488 (2009). · DOI · Paper page
  7. Geiger, T., Cox, J. & Mann, M. Proteomics on an Orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol. Cell. Proteomics 9, 2252–2261 (2010). · DOI · Paper page
  8. Egertson, J.D. et al. Multiplexed MS/MS for improved data-independent acquisition. Nat. Methods 10, 744–746 (2013). · DOI · Paper page
  9. Distler, U. et al. Drift time-specific collision energies enable deep-coverage data-independent acquisition proteomics. Nat. Methods 11, 167–170 (2014). · DOI · Paper page
  10. Purvine, S., Eppel, J.T., Yi, E.C. & Goodlett, D.R. Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer. Proteomics 3, 847–850 (2003). · DOI · Paper page
  11. Colangelo, C.M., Chung, L., Bruce, C. & Cheung, K.H. Review of software tools for design and analysis of large scale MRM proteomic datasets. Methods 61, 287–298 (2013). · DOI
  12. Röst, H.L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014). · DOI · Paper page
  13. Rosenberger, G. et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data 1, 140031 (2014). · DOI · Paper page
  14. Li, G.Z. et al. Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics 9, 1696–1719 (2009). · DOI · Paper page
  15. Pak, H. et al. Clustering and filtering tandem mass spectra acquired in data-independent mode. J. Am. Soc. Mass Spectrom. 24, 1862–1871 (2013). · DOI
  16. Craig, R., Cortens, J.P. & Beavis, R.C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234–1242 (2004). · DOI · Paper page
  17. Eng, J.K., Jahan, T.A. & Hoopmann, M.R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013). · DOI · Paper page
  18. Kim, S. et al. The generating function of CID, ETD, and CID/ETD pairs of tandem mass spectra: applications to database search. Mol. Cell. Proteomics 9, 2840–2852 (2010). · DOI · Paper page
  19. Keller, A., Nesvizhskii, A.I., Kolker, E. & Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383–5392 (2002). · DOI · Paper page
  20. Shteynberg, D. et al. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteomics 10, M111.007690 (2011). · DOI · Paper page
  21. Nesvizhskii, A.I., Keller, A., Kolker, E. & Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–4658 (2003). · DOI · Paper page
  22. Lam, H. et al. Building consensus spectral libraries for peptide identification in proteomics. Nat. Methods 5, 873–875 (2008). · DOI · Paper page
  23. Liu, Y. et al. Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness. Mol. Cell. Proteomics 13, 1753–1768 (2014). · DOI · Paper page
  24. Ludwig, C., Claassen, M., Schmidt, A. & Aebersold, R. Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol. Cell. Proteomics 11, M111.013987 (2012). · DOI
  25. Nesvizhskii, A.I. Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments. Proteomics 12, 1639–1655 (2012). · DOI
  26. Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 8, 70–73 (2011). · DOI · Paper page
  27. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816–D823 (2013). · DOI · Paper page
  28. Jeronimo, C. et al. Systematic analysis of the protein interaction network for the human transcription machinery reveals the identity of the 7SK capping enzyme. Mol. Cell 27, 262–274 (2007). · DOI · Paper page
  29. Prakash, A. et al. Hybrid data acquisition and processing strategies with increased throughput and selectivity: pSMART analysis for global qualitative and quantitative analysis. J. Proteome Res. 13, 5415–5430 (2014). · DOI
  30. MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010). · DOI · Paper page
  31. Chambers, M.C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012). · DOI · Paper page
  32. Tautenhahn, R., Bottcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504 (2008). · DOI · Paper page
  33. Nesvizhskii, A.I. et al. Dynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides. Mol. Cell. Proteomics 5, 652–670 (2006). · DOI · Paper page
  34. Kryuchkov, F., Verano-Braga, T., Hansen, T.A., Sprenger, R.R. & Kjeldsen, F. Deconvolution of mixture spectra and increased throughput of peptide identification by utilization of intensified complementary ions formed in tandem mass spectrometry. J. Proteome Res. 12, 3362–3371 (2013). · DOI
  35. Deutsch, E.W. et al. A guided tour of the Trans-Proteomic Pipeline. Proteomics 10, 1150–1159 (2010). · DOI · Paper page
  36. Tsou, C.C. et al. IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation. Mol. Cell. Proteomics 9, 131–144 (2010). · DOI
  37. Lam, H., Deutsch, E.W. & Aebersold, R. Artificial decoy spectral libraries for false discovery rate estimation in spectral library searching in proteomics. J. Proteome Res. 9, 605–610 (2010). · DOI
  38. Cox, J., Michalski, A. & Mann, M. Software lock mass by two-dimensional minimization of peptide mass errors. J. Am. Soc. Mass Spectrom. 22, 1373–1380 (2011). · DOI
  39. Barsnes, H. et al. compomics-utilities: an open-source Java library for computational proteomics. BMC Bioinformatics 12, 70 (2011). · DOI
  40. Escher, C. et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111–1121 (2012). · DOI · Paper page
  41. Vizcaíno, J.A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223–226 (2014). · DOI · Paper page

Cited by

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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