Skip to main content
SpringerLink
Log in
Book cover

Serum/Plasma Proteomics pp 349–374Cite as

Using the Human Plasma PeptideAtlas to Study Human Plasma Proteins

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 728))

Abstract

PeptideAtlas is a web-accessible database of LC–MS/MS shotgun proteomics results from hundreds of experiments conducted in diverse laboratories, with all data processed via a uniform analysis pipeline. A total of 91 experiments on human plasma and serum are included. Using the PeptideAtlas web interface, users can browse and search the Human Plasma PeptideAtlas for identified peptides and identified proteins, view spectra, and select proteotypic peptides. Users can easily view supporting information such as chromosomal mapping, estimated abundances, and sequence alignments. Herein, the reader is instructed in the use of the Human Plasma PeptideAtlas through an illustrated exploration of cytokine receptors in plasma.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

Notes

  1. 1.

     As of this writing, the Sample Contribution table shows data reflecting the number of multiply observed peptides, a vestige of the time when the Trans-Proteomic Pipeline was less good at distinguishing between true and false hits; at that time we believed that peptides observed only once were not reliable enough.

References

  1. Aebersold, R., and Mann, M. (2003) Mass spectrometry-based proteomics. Nature 422, 198–207.

    Article  PubMed  CAS  Google Scholar 

  2. Hanash, S., and Celis, J. E. (2002) The Human Proteome Organization: a mission to advance proteome knowledge. Mol Cell Proteomics 1, 413–4.

    Article  PubMed  CAS  Google Scholar 

  3. Omenn, G. S. (2004) The Human Proteome Organization Plasma Proteome Project pilot phase: reference specimens, technology platform comparisons, and standardized data submissions and analyses. Proteomics 4, 1235–40.

    Article  PubMed  CAS  Google Scholar 

  4. Omenn, G. S., States, D. J., Adamski, M. et al. (2005) Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226–45.

    Article  PubMed  CAS  Google Scholar 

  5. Omenn, G. S., Aebersold, R., and Paik, Y. K. (2009) 7(th) HUPO World Congress of Proteomics: launching the second phase of the HUPOPlasma Proteome Project (PPP-2) 16-20 August 2008, Amsterdam, The Netherlands. Proteomics 9, 4–6.

    Article  PubMed  CAS  Google Scholar 

  6. Desiere, F., Deutsch, E. W., King, N. L. et al. (2006) The PeptideAtlas project. Nucleic Acids Res 34, D655–8.

    Article  PubMed  CAS  Google Scholar 

  7. Farrah, T., Deutsch, E. W., Omenn, G. S., et al. A high-confidence human plasma proteome reference with estimated concentrations in PeptideAtlas, submitted.

    Google Scholar 

  8. Eng, J., McCormack, A. L., and Yates, J. R. (1994) 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.

    Article  CAS  Google Scholar 

  9. Lam, H., Deutsch, E. W., Eddes, J. S. et al. (2007) Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7, 655–67.

    Article  PubMed  CAS  Google Scholar 

  10. Boeckmann, B., Bairoch, A., Apweiler, R. et al. (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 31, 365–70.

    Article  PubMed  CAS  Google Scholar 

  11. Hubbard, T. J., Aken, B. L., Beal, K. et al. (2007) Ensembl 2007. Nucleic Acids Res 35, D610–7.

    Article  PubMed  CAS  Google Scholar 

  12. Kersey, P. J., Duarte, J., Williams, A. et al. (2004) The International Protein Index: an integrated database for proteomics experiments. Proteomics 4, 1985–8.

    Article  PubMed  CAS  Google Scholar 

  13. Keller, A., Eng, J., Zhang, N., Li, X. J., and Aebersold, R. (2005) A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 1, 2005 0017.

    PubMed  Google Scholar 

  14. Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74, 5383–5392.

    Article  PubMed  CAS  Google Scholar 

  15. Shteynberg, D., Deutsch, E. W., Lam, H., et al. iProphet: Improved statistical validation of peptide identifications in shotgun proteomics, submitted.

    Google Scholar 

  16. Nesvizhskii, A. I., Keller, A., Kolker, E., and Aebersold, R. (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal Chem 75, 4646–4658.

    Article  PubMed  CAS  Google Scholar 

  17. Deutsch, E. W. (2010) The PeptideAtlas Project. Methods in Molecular Biology 604, 319–331.

    Article  PubMed  CAS  Google Scholar 

  18. National Institute of Standards and Technology.

    Google Scholar 

  19. Shannon, P., Markiel, A., Ozier, O. et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13, 2498–504.

    Article  PubMed  CAS  Google Scholar 

  20. Mallick, P., Schirle, M., Chen, S. S. et al. (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 25, 125–31.

    Article  PubMed  CAS  Google Scholar 

  21. Tang, H., Arnold, R. J., Alves, P. et al. (2006) A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics 22, e481–8.

    Article  PubMed  CAS  Google Scholar 

  22. States, D. J., Omenn, G. S., Blackwell, T. W. et al. (2006) Challenges in deriving high-­confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol 24, 333–8.

    Article  PubMed  CAS  Google Scholar 

  23. ZLu, P., Vogel, C., Wang, R., et al. (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol 25, 117–124.

    Google Scholar 

  24. Deutsch, E. W., Lam, H., and Aebersold, R. (2008) PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep 9, 429–34.

    Article  PubMed  CAS  Google Scholar 

  25. Sherwood, C. A., Eastham, A., Lee, L. W. et al. (2009) MaRiMba: a software application for spectral library-based MRM transition list assembly. J Proteome Res 8, 4396–405.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

The PeptideAtlas Project has involved a great many contributors. The authors would like to thank the following persons for their contributions to the design and implementation of PeptideAtlas: Dave Campbell, Nichole King, Luis Mendoza, David Shteynberg, Natalie Tasman, Abhishek Pratap, Pat Moss, Jimmy Eng, Ning Zhang, Frank Desiere, Zhi Sun, and Michael Johnson. The authors would also like to thank Christopher Paulse and Robert West for reviewing this chapter and Julie Bletz for editing.

The authors have been funded in part with Federal funds from the National Heart, Lung, and Blood Institute, and the National Institutes of Health, under contract No. N01-HV-28179, and from PM50 GMO76547/Center for Systems Biology.

Author information

Authors and Affiliations

  1. Institute for Systems Biology, Seattle, WA, USA

    Terry Farrah

Authors
  1. Terry Farrah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric W. Deutsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruedi Aebersold
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Terry Farrah .

Editor information

Editors and Affiliations

  1. Ludwig Inst. for Cancer Research, Joint Proteomics Laboratory (JPSL), Royal Melbourne Hospital, Royal Parade, Parkville, 3052, Victoria, Australia

    Richard J. Simpson

  2. Ludwig Inst. for Cancer Research, Joint Proteomics Laboratory, Royal Melbourne Hospital, Royal Parade, Parkville, 3050, Victoria, Australia

    David W. Greening

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Farrah, T., Deutsch, E.W., Aebersold, R. (2011). Using the Human Plasma PeptideAtlas to Study Human Plasma Proteins. In: Simpson, R., Greening, D. (eds) Serum/Plasma Proteomics. Methods in Molecular Biology, vol 728. Humana Press. https://doi.org/10.1007/978-1-61779-068-3_23

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-068-3_23

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-067-6

  • Online ISBN: 978-1-61779-068-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation