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Bioinformatics Challenges in the Proteomic Analysis of Human Plasma

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Serum/Plasma Proteomics

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

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Abstract

Mass spectrometry has become the method of choice for studying proteins in complex mixtures in a qualitative and quantitative fashion. The application of mass spectrometry-based proteomics analyses on plasma has correspondingly been established as an important method for disease-associated biomarker discovery and validation. Yet despite being a readily available human sample, plasma poses several important challenges to the proteomics researcher. With a focus on bioinformatics aspects, this chapter will discuss the problems involved in analyzing plasma proteomics data, along with the scope of solutions available through specialised tools and sophisticated analysis methods.

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References

  1. Chiras, D., D. (2005) Human Biology, 5th Edition

    Google Scholar 

  2. Anderson NL. (2003) The Human Plasma Proteome: History, Character, and Diagnostic Prospects. Mol. Cell. Proteomics 1, 845–867.

    Google Scholar 

  3. Omenn GS. (2004) The Human Proteome Organization Plasma Proteome Project pilot phase: Reference specimens, technology ­platform comparisons, and standardised data submissions and analyses. Proteomics 4, 1235–1240.

    Article  PubMed  CAS  Google Scholar 

  4. Rai AJ, Gelfand Ca, Haywood BC, et al. (2005) HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma proteome samples. Proteomics 5, 3262–77.

    Article  PubMed  CAS  Google Scholar 

  5. de Roos B, Duthie SJ, Polley AC, et al. (2008) Proteomic methodological recommendations for studies involving human plasma, platelets, and peripheral blood mononuclear cells. J. Proteome Res. 7, 2280–90.

    Article  PubMed  Google Scholar 

  6. Omenn GS, States DJ, 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 

  7. Martens L, Hermjakob H, Jones P, et al. (2005) PRIDE: the proteomics identifications database. Proteomics 5, 3537–45.

    Article  PubMed  CAS  Google Scholar 

  8. Omenn GS, Aebersold R, Paik Y. (2009) 7th 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 

  9. Sickmann A, et al. (2003) The proteome of Saccharomyces cerevisiae mitochondria. PNAS 100, 13207–13212.

    Article  PubMed  CAS  Google Scholar 

  10. Gevaert K, Van Damme P, Ghesquière B, et al. (2007) A la carte proteomics with an emphasis on gel-free techniques. Proteomics 7, 2698–718.

    Article  PubMed  CAS  Google Scholar 

  11. Klie S, Martens L, Vizcaíno JA, et al. (2008) Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing. J. Proteome Res. 7, 182–191.

    Article  PubMed  CAS  Google Scholar 

  12. Stempfer R, Kubicek M, Lang IM, Christa N, Gerner C. (2008) Quantitative assessment of human serum high-abundance protein depletion. Electrophoresis 29, 4316–23.

    Article  PubMed  CAS  Google Scholar 

  13. Deutsch EW, Eng JK, Zhang H, et al. (2005) Human Plasma PeptideAtlas. Proteomics 5, 3497–500.

    Article  PubMed  CAS  Google Scholar 

  14. Sadygov RG, Cociorva D, Yates JR. (2004) Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book. Nat. Meth. 1, 195–2022.

    Article  CAS  Google Scholar 

  15. Nesvizhskii AI, Vitek O, Aebersold R. (2007) Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797.

    Article  PubMed  CAS  Google Scholar 

  16. Martens L, Hermjakob H. (2007) Proteomics data validation: why all must provide data. Molecular bioSystems 3, 518–22.

    Article  PubMed  CAS  Google Scholar 

  17. Nesvizhskii AI, Keller A, Kolker E, Aebersold R. (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646–58.

    Article  PubMed  CAS  Google Scholar 

  18. He F. Human liver proteome project: plan, progress, and perspectives. (2005) Mol. Cell. Proteomics 4, 1841–8.

    Google Scholar 

  19. The UniProt Consortium. The Universal Protein Resource (UniProt) 2009. (2009) Nucleic acids research 37, D169–74.

    Google Scholar 

  20. Kapp EA, Schütz F, Connolly LM, et al. (2005) An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: sensitivity and specificity analysis. Proteomics 5, 3475–90.

    Article  PubMed  CAS  Google Scholar 

  21. Balgley BM, Laudeman T, Yang L, Song T, Lee CS. (2007) Comparative evaluation of tandem MS search algorithms using a target-decoy search strategy. Mol. Cell. Proteomics 6, 1599–608.

    Article  PubMed  CAS  Google Scholar 

  22. http://www.openms.de

  23. Stephan C, Reidegeld KA, Hamacher M, et al. (2006) Automated reprocessing pipeline for searching heterogeneous mass spectrometric data of the HUPO Brain Proteome Project pilot phase. Proteomics 6, 5015–29.

    Article  PubMed  CAS  Google Scholar 

  24. Martens L, Müller M, Stephan C, et al. (2006) A comparison of the HUPO Brain Proteome Project pilot with other proteomics studies. Proteomics 6, 5076–86.

    Article  PubMed  CAS  Google Scholar 

  25. Yi J, Kim C, Gelfand Ca. (2007) Inhibition of Intrinsic Proteolytic Activities Moderates Preanalytical Variability and Instability of Human Plasma. J. Proteome Res. 6, 1768–1781.

    Article  PubMed  CAS  Google Scholar 

  26. Reisinger F, Martens L. (2009) Database on Demand - An online tool for the custom generation of FASTA-formatted sequence databases. Proteomics 9, 4421–4424.

    Article  PubMed  CAS  Google Scholar 

  27. Martens L. (2005) DBToolkit: processing protein databases for peptide-centric proteomics. Bioinformatics 21, 3584–3585.

    Article  PubMed  CAS  Google Scholar 

  28. Kersey PJ, 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 

  29. Gevaert K, Goethals M, Martens L, et al. (2003) Exploring proteomes and analyzing protein processing by mass spectrometric identification of sorted N-terminal peptides. Nature Biotechnology 21, 566–569.

    Article  PubMed  CAS  Google Scholar 

  30. Meek D. (1998) Multisite Phosphorylation and the Integration of Stress Signals at p53. Cellular Signalling 10, 159–166.

    Article  PubMed  CAS  Google Scholar 

  31. Creasy DM, Cottrell JS. (2004) Unimod: Protein modifications for mass spectrometry. Proteomics 4, 1534–1536.

    Article  PubMed  CAS  Google Scholar 

  32. Montecchi-Palazzi L, Beavis R, Binz P, et al. (2008) The PSI-MOD community standard for representation of protein modification data. Nature Biotechnology 26, 864–866.

    Article  PubMed  CAS  Google Scholar 

  33. Gornik O, Lauc G. (2008) Glycosylation of serum proteins in inflammatory diseases. Disease markers 25, 267–78.

    PubMed  CAS  Google Scholar 

  34. Yates JR, Eng JK, McCormack AL, Schieltz D. (1995) Method to correlate tandem mass spectra of modified peptides to amino acid sequences in the protein database. Anal. Chem. 67, 1426–36.

    Article  PubMed  CAS  Google Scholar 

  35. Craig R, Beavis RC. (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466–7.

    Article  PubMed  CAS  Google Scholar 

  36. Creasy DM, Cottrell JS. (2002) Error tolerant searching of uninterpreted tandem mass spectrometry data. Proteomics 2,1426–34.

    Article  PubMed  CAS  Google Scholar 

  37. Pevzner PA, Tang CL. (2000) Mutation-Tolerant Protein Identification by Mass Spectrometry. Journal of Computational Biology 7, 777–787.

    Article  PubMed  CAS  Google Scholar 

  38. Tsur D, Tanner S, Zandi E, Bafna V, Pevzner PA. (2005) Identification of post-translational modifications by blind search of mass spectra. Nat. Biotech. 23, 1562–7.

    Article  CAS  Google Scholar 

  39. Tanner S, Pevzner Pa, Bafna V. (2006) Unrestrictive identification of post-translational modifications through peptide mass spectrometry. Nat. Prot. 1, 67–72.

    Article  CAS  Google Scholar 

  40. Falkner Ja, Falkner JW, Yocum AK, Andrews PC. (2008) A spectral clustering approach to MS/MS identification of post-translational modifications. J. Proteome Res. 7, 4614–22.

    Article  Google Scholar 

  41. Savitski MM, Nielsen ML, Zubarev Ra. (2006) ModifiComb, a new proteomic tool for mapping substoichiometric post-translational modifications, finding novel types of modifications, and fingerprinting complex protein mixtures. Mol. Cell. Proteomics 5, 935–48.

    Article  PubMed  CAS  Google Scholar 

  42. Mann M, Wilm M. (1994) Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal. Chem. 66, 4390–9.

    Article  PubMed  CAS  Google Scholar 

  43. Tabb DL, Saraf A, Yates JR. (2003) GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model. Anal. Chem. 75, 6415–21.

    Article  PubMed  CAS  Google Scholar 

  44. Tabb DL, Ma Z, Martin DB, Ham AL, Chambers MC. (2008) DirecTag: accurate sequence tags from peptide MS/MS through statistical scoring. J. Proteome Res. 7, 3838–46.

    Article  PubMed  CAS  Google Scholar 

  45. Gevaert K, Impens F, Ghesquière B, et al. (2008) Stable isotopic labelling in proteomics. Proteomics, 8, 4873–85.

    Article  PubMed  CAS  Google Scholar 

  46. Echan LA, Tang H, Ali-Khan N, Lee K, Speicher DW. (2005) Depletion of multiple high-abundance proteins improves protein profiling capacities of human serum and plasma. Proteomics 5, 3292–303.

    Article  PubMed  CAS  Google Scholar 

  47. Mallick P, Schirle M, Chen SS, et al. (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat. Biotech. 25, 125–131.

    Article  CAS  Google Scholar 

  48. Lange V, Picotti P, Domon B, Aebersold R. (2008) Selected reaction monitoring for quantitative proteomics: a tutorial. Molecular systems biology 4, 222.

    Article  PubMed  Google Scholar 

  49. Mead Ja, Bianco L, Ottone V, et al. (2009) MRMaid, the web-based tool for designing multiple reaction monitoring (MRM) transitions. Mol. Cell. Proteomics 8, 696–705.

    Article  Google Scholar 

  50. Sherwood Ca, Eastham A, Lee LW, et al. (2009) MaRiMba: A Software Application for Spectral Library-Based MRM Transition List Assembly. J. Proteome Res. 8, 4396–405.

    Article  Google Scholar 

  51. Zakett D, Flynn RG, Cooks RG. (1978) Chlorine isotope effects in mass spectrometry by multiple reaction monitoring. J. Physical Chem. 82, 2359–2362.

    Article  CAS  Google Scholar 

  52. Anderson NL, Anderson NG, Pearson TW, et al. (2009) A human proteome detection and quantitation project. Mol. Cell. Proteomics 8, 883–6.

    Article  PubMed  CAS  Google Scholar 

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Author information

Authors and Affiliations

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

    Joseph M. Foster

Authors
  1. Joseph M. Foster
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  2. Lennart Martens
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Corresponding author

Correspondence to Joseph M. Foster .

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

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Foster, J.M., Martens, L. (2011). Bioinformatics Challenges in the Proteomic Analysis of Human Plasma. 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_22

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  • DOI: https://doi.org/10.1007/978-1-61779-068-3_22

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-067-6

  • Online ISBN: 978-1-61779-068-3

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