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A common open representation of mass spectrometry data and its application to proteomics research

Nature Biotechnology volume 22, pages 1459–1466 (2004)Cite this article

Abstract

A broad range of mass spectrometers are used in mass spectrometry (MS)-based proteomics research. Each type of instrument possesses a unique design, data system and performance specifications, resulting in strengths and weaknesses for different types of experiments. Unfortunately, the native binary data formats produced by each type of mass spectrometer also differ and are usually proprietary. The diverse, nontransparent nature of the data structure complicates the integration of new instruments into preexisting infrastructure, impedes the analysis, exchange, comparison and publication of results from different experiments and laboratories, and prevents the bioinformatics community from accessing data sets required for software development. Here, we introduce the 'mzXML' format, an open, generic XML (extensible markup language) representation of MS data. We have also developed an accompanying suite of supporting programs. We expect that this format will facilitate data management, interpretation and dissemination in proteomics research.

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Figure 1: The mzXML file acts as a mediator, allowing multiple input formats to be subjected to a common data analysis pipeline.
Figure 2: Overview of the mzXML format.
Figure 3: Although most mass spectrometers are capable of exporting data in formats recognized by sequence search engines (e.g., .dta for SEQUEST, .mgf for Mascot and .pkl for ProteinLynx), other data analysis operations, such as peptide quantification after stable isotope labeling, are not supported by these formats.
Figure 4: Various methods for visualization of data contained in mzXML documents.
Figure 5: Role of the mzXML format in an analysis framework.
Figure 6: Standard versus mzXML database search results.

References

  1. Patterson, S.D. & Aebersold, R.H. Proteomics: the first decade and beyond. Nat. Genet. 33 suppl., 311–323 (2003).

    Article  CAS  PubMed  Google Scholar 

  2. Spellman, P.T. et al. Design and implementation of microarray gene expression markup language (MAGE-ML). Genome Biol. 3, RESEARCH0046 (2002).

  3. Li, X., Zhang, H., Ranish, J.A. & Aebersold, R. Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal. Chem. 75, 6658–6665 (2003).

    Article  CAS  Google Scholar 

  4. Han, D.K., Eng, J., Zhou, H. & Aebersold, R. Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat. Biotechnol. 19, 946–951 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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).

    Article  CAS  PubMed  Google Scholar 

  6. Eng, J.K., McCormack, A.L. & Yates, J.R. III. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).

    Article  CAS  PubMed  Google Scholar 

  7. Perkins, D.N., Pappin, D.J., Creasy, D.M. & Cottrell, J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    Article  CAS  PubMed  Google Scholar 

  8. Li, X.J. et al. A tool to visualize and evaluate data obtained by liquid chromatography-electrospray ionization-mass spectrometry. Anal. Chem. 76, 3856–3860 (2004).

    Article  CAS  PubMed  Google Scholar 

  9. Gygi, S.P. et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat. Biotechnol. 17, 994–999 (1999).

    Article  CAS  PubMed  Google Scholar 

  10. 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).

    Article  CAS  PubMed  Google Scholar 

  11. 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).

    Article  CAS  PubMed  Google Scholar 

  12. Baldwin, M.A. Protein identification by mass spectrometry: issues to be considered. Mol. Cell. Proteomics 3, 1–9 (2004).

    Article  CAS  PubMed  Google Scholar 

  13. Prince, J.T., Carlson, M.W., Wang, R., Lu, P. & Marcotte, E.M. The need for a public proteomics repository. Nat. Biotechnol. 22, 471–472 (2004).

    Article  CAS  PubMed  Google Scholar 

  14. Zhang, N., Aebersold, R. & Schwikowski, B. ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data. Proteomics 2, 1406–1412 (2002).

    Article  CAS  PubMed  Google Scholar 

  15. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This project was funded in part by federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, under contract no. N01-HV-28179 and by grant no. 1R33CA93302 from the National Cancer Institute. The Institute for Systems Biology is supported by a generous gift from Merck and Co. We are grateful to SourceForge for hosting the project and Eugene Yi for providing the seven-protein mix data set. We would also like to acknowledge the following for endorsing the mzXML format: Philip C. Andrews, Tom Blackwell, Daniel Burns, Jayson Falkner, Panagiotis Papoulias, Abhik Shah, Peter Ulintz, Al Burlingame, Robert Chalkley, Karl Clauser, Bruno Domon, James Eddes, Robert Moritz, Daniel Figeys, Barry L. Karger, William Hancock, Tomas Rejtar, Peter James, Matthias Mann, Sanford Markey, Matthias Wilm, Ken Williams and Kratos Analytical Limited (a Shimadzu Group Company).

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Authors and Affiliations

  1. Institute for Systems Biology, 1441 North 34 Street, Seattle, 98103-8904, Washington, USA

    Patrick G A Pedrioli, Jimmy K Eng, Robert Hubley, Mathijs Vogelzang, Eric W Deutsch, Brian Raught & Ruedi Aebersold

  2. Insilicos LLC, 4509 Interlake Avenue North, no. 223, Seattle, 98103-6773, Washington, USA

    Brian Pratt & Erik Nilsson

  3. Albert Einstein College of Medicine, LMAP Room 405, Ullman Bldg., 1300 Morris Park Avenue, Bronx, 10461, New York, USA

    Ruth H Angeletti

  4. EMBL Outstation European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

    Rolf Apweiler, Henning Hermjakob, Chris F Taylor & Weimin Zhu

  5. Department of Anesthesiology, Center for Medical Informatics, Yale University School of Medicine, PO Box 208009, New Haven, 06520, Connecticut, USA

    Kei Cheung

  6. Boston University School of Medicine, 715 Albany Street, R-806, Boston, 02118-2526, Massachusetts, USA

    Catherine E Costello, Sequin Huang & Mark E McComb

  7. Lilly Research Laboratories, One Lilly Corporate Center, Indianapolis, 46285, Indiana, USA

    Randall K Julian Jr

  8. Joint Proteomics Laboratory, Ludwig Institute For Cancer Research & The Walter and Eliza Hall Institute of Medical Research, Royal Melbourne Hospital, Parkville, Victoria, 3050, Australia

    Eugene Kapp & Richard Simpson

  9. School of Biological Sciences, University of Manchester, The Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK

    Stephen G Oliver

  10. The University of Michigan Medical School, 1150 W. Medical Center Drive, Ann Arbor, 48109-0656, Michigan, USA

    Gilbert Omenn

  11. Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK

    Norman W Paton

  12. Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, PO Box 999, Richland, 99352, Washington, USA

    Richard Smith

Authors
  1. Patrick G A Pedrioli
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  15. Randall K Julian Jr
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  16. Eugene Kapp
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  18. Stephen G Oliver
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  19. Gilbert Omenn
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  20. Norman W Paton
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  21. Richard Simpson
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  22. Richard Smith
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  23. Chris F Taylor
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  24. Weimin Zhu
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  25. Ruedi Aebersold
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Corresponding author

Correspondence to Ruedi Aebersold.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

The PEDRo model and the mzXML format. (PDF 420 kb)

Supplementary Fig. 2

The mzXML index. (PDF 19 kb)

Supplementary Methods (PDF 11 kb)

Supplementary Notes

Quick introduction to XML. (PDF 21 kb)

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Pedrioli, P., Eng, J., Hubley, R. et al. A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol 22, 1459–1466 (2004). https://doi.org/10.1038/nbt1031

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