To the editor:

A paper in last August's issue presented the overall Minimum Information about a Proteomics Experiment (MIAPE) reporting guidelines for the field of proteomics1. We would like to draw the community's attention to the MIAPE Mass Spectrometry (MIAPE-MS) module, which specifies the minimum information that should be provided when reporting the use of mass spectrometry in a proteomics experiment (Box 1). Developed through a joint effort between the Mass Spectrometry working group of the Human Proteome Organisation's Proteomics Standards Initiative (HUPO-PSI; and the wider proteomics community, MIAPE-MS constitutes one part of the MIAPE documentation system. It comprises a checklist of information that should be provided about mass spectrometry performed in the course of generating a data set that is submitted to a public repository or when such an experimental step is reported in a scientific publication (for instance, in the materials and methods section). MIAPE-MS specifies neither the format in which information should be transferred nor the structure of any repository or document. However, HUPO-PSI is not developing the MIAPE modules in isolation; several compatible data exchange standards are now well established and supported both by public databases and by data processing software in proteomics.

Box 1: Content snapshot for MIAPE-MS

The full MIAPE-MS document is divided into three parts: An introduction providing background and context; a summary list of the items to be reported; and a glossary with definitions and examples.

The MIAPE-MS guidelines themselves are subdivided as follows:

  1. General features. Summary information such as instrument manufacturer, the software used to run the machine and the parameters applied to it.

  2. Ion source. For example, electrospray ionization (ESI) or matrix-assisted laser desorption ionization (MALDI).

  3. All major components after the ion source. For example, ion traps, collision cells, time-of-flight tubes and detectors. Note that if a collision cell is also an ion trap (e.g., Fourier Transform Ion Cyclotron Resonance, or FT-ICR, cells), the guidelines for the relevant components should be combined.

  4. The data resulting from the procedure, the method of generation of peak lists and the location of the raw data from which they were generated, the method by which quantification was performed (where appropriate) and the resulting quantitative data set.

The modern mass spectrometer is a rather complex instrument with many operational parameters; the data sets generated are similarly complex and often rather voluminous. Therefore, MIAPE-MS does not prescribe that all of that information be captured; and given the diversity of instruments currently available, the utility of such detail is clearly open to question. However, it is possible to specify parameters that are representative of the way in which the mass spectrometer was used, to contextualize the data generated and thereby enable a better-informed process of assessment and interpretation.

The guidelines (see Supplementary Guidelines and Supplementary Table 1 online) cover both the operation of a mass spectrometer and the generation of mass spectra from the 'raw' data. They do not cover the delivery of sample to the mass spectrometer or the interpretation of spectra by search engines; those details are captured in separate MIAPE modules, the latest versions of which can be obtained from the MIAPE home page. Note also that MIAPE-MS does not cover all the available components of a mass spectrometer (e.g., some of the less frequently used ion sources); subsequent versions may have expanded coverage, as will almost certainly be the case for all MIAPE modules.

These guidelines will evolve as circumstances dictate. The most recent version of MIAPE-MS is available at and the content is replicated here as supplementary information (Supplementary Guidelines and Supplementary Table 1). To contribute, or to track the process to remain 'MIAPE compliant', browse to the website at

Note: Supplementary information is available on the Nature Biotechnology website.


  1. 1.

    et al. Nat. Biotechnol. 25, 887–893 (2007).

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Author information


  1. European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK.

    • Chris F Taylor
  2. NERC Environmental Bioinformatics Centre, Mansfield Road, Oxford, OX1 3SR, UK.

    • Chris F Taylor
  3. Swiss Institute of Bioinformatics, Proteome Informatics Group, Rue Michel-Servet 1, CH-1211 Geneva 4, Switzerland.

    • Pierre-Alain Binz
  4. GeneBio SA, 25 Av. de Champel, CH-1206 Geneva, Switzerland.

    • Pierre-Alain Binz
  5. Institute for Molecular Systems Biology, ETH Zurich, HPT E 78, Wolfgang-Pauli-Str. 16, 8093 Zürich, Switzerland.

    • Ruedi Aebersold
  6. Nestlé Research Center, Nestec Ltd., Vers-chez-Les-Blanc, 1000 Lausanne 26, Switzerland.

    • Michel Affolter
    •  & Martin Kussmann
  7. Affimetrix, Inc., 3420 Central Expressway, Santa Clara, California 95051, USA.

    • Robert Barkovich
  8. Institute for Systems Biology, 1441 N. 34th Street, Seattle, Washington 98103, USA.

    • Eric W Deutsch
  9. Agilent Technologies, 5301 Stevens Creek Blvd., Santa Clara, California 95051, USA.

    • David M Horn
  10. Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, USA.

    • Andreas Hühmer
  11. Cambridge Centre for Proteomics, University of Cambridge, Cambridge, Cambridgeshire, CB2 1QW, UK.

    • Kathryn Lilley
  12. Bruker Daltonik GmbH, Bremen, Germany.

    • Marcus Macht
  13. Department of Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.

    • Matthias Mann
  14. Novartis Institutes for BioMedical Research, Developmental and Molecular Pathways, Pathway Profiling, WSJ-088.702, CH-4056 Basel, Switzerland.

    • Dieter Müller
  15. Skirball Institute of Biomolecular Medicine and Department of Pharmacology, New York University School of Medicine, New York, New York 10016, USA.

    • Thomas A Neubert
  16. Pathways, DECS, AstraZeneca, Alderley Park, Macclesfield, Cheshire, SK10 4TF, UK.

    • Janice Nickson
  17. Amgen Inc., Molecular Sciences, One Amgen Center Drive MS 38-3-A, Thousand Oaks, California 91320-1799, USA.

    • Scott D Patterson
  18. Kratos Analytical (Shimadzu), Wharfside, Trafford Wharf Road, Manchester, M17 1GP UK.

    • Roberto Raso
  19. Department of Chemistry & Biochemistry, University of Colorado, Boulder, Colorado 80309-0215, USA.

    • Kathryn Resing
  20. Applied Biosystems, 850 Lincoln Centre Drive, Foster City, California 94404, USA.

    • Sean L Seymour
  21. Proteomics Research Laboratory, Tokyo Rikakikai Co., Tsukuba, Japan.

    • Akira Tsugita
  22. Swiss Institute of Bioinformatics, Vital-IT group, Quartier Sorge – Bàtiment Génopole, CH-1015 Lausanne, Switzerland.

    • Ioannis Xenarios
  23. Institutes for Biological Sciences, Chinese Academy of Sciences, Graduate School of the Chinese Academy of Sciences, 320 Yue-Yang Road, Shanghai 200031, China.

    • Rong Zeng
  24. Indigo BioSystems, Inc., 111 Congressional Blvd., Suite 160, Carmel, Indiana 46032, USA.

    • Randall K Julian Jr


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Corresponding author

Correspondence to Pierre-Alain Binz.

Supplementary information

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  1. 1.

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    SupplementaryTable 1, Guidelines

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