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Bioinformatics for Proteomics: Opportunities at the Interface Between the Scientists, Their Experiments, and the Community

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Shotgun Proteomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1156))

Abstract

Within the last decade, bioinformatics has moved from command line scripts dedicated to single experiments towards production grade software integrated in experimental workflows providing a rich environment for biological investigation. Located at the interface between the scientists, their experiments, and the community, bioinformatics acts as a gateway to a wide source of information. This chapter does not list tools and methods, but rather hints at how bioinformatics can help in improving biological projects, all the way from their initial design to the dissemination of the results.

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References

  1. Bromenshenk JJ, Henderson CB, Wick CH et al (2010) Iridovirus and microsporidian linked to honey bee colony decline. PLoS One 5:e13181

    Article  PubMed Central  PubMed  Google Scholar 

  2. Foster LJ (2011) Interpretation of data underlying the link between colony collapse disorder (CCD) and an invertebrate iridescent virus. Mol Cell Proteomics 10:M110.006387

    Article  PubMed Central  PubMed  Google Scholar 

  3. Ma K, Vitek O, Nesvizhskii AI (2012) A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet. BMC Bioinformatics 13 Suppl 16:S1

    Google Scholar 

  4. Vaudel M, Burkhart JM, Sickmann A et al (2011) Peptide identification quality control. Proteomics 11:2105–2114

    Article  CAS  PubMed  Google Scholar 

  5. Colaert N, Degroeve S, Helsens K et al (2011) Analysis of the resolution limitations of peptide identification algorithms. J Proteome Res 10:5555–5561

    Article  CAS  PubMed  Google Scholar 

  6. Knudsen GM, Chalkley RJ (2011) The effect of using an inappropriate protein database for proteomic data analysis. PLoS One 6:e20873

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Szklarczyk D, Franceschini A, Kuhn M et al (2011) The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 39:D561–D568

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Kerrien S, Aranda B, Breuza L et al (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40:D841–D846

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Sherman BT, da Huang W, Tan Q et al (2007) DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics 8:426

    Article  PubMed Central  PubMed  Google Scholar 

  10. Haw R, Hermjakob H, D’Eustachio P et al (2011) Reactome pathway analysis to enrich biological discovery in proteomics data sets. Proteomics 11:3598–3613

    Article  CAS  PubMed  Google Scholar 

  11. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Hornbeck PV, Kornhauser JM, Tkachev S et al (2012) PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res 40:D261–D270

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Reddy TB, Riley R, Wymore F et al (2009) TB database: an integrated platform for tuberculosis research. Nucleic Acids Res 37:D499–D508

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Forbes SA, Bindal N, Bamford S et al (2011) COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res 39:D945–D950

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Apweiler R, Bairoch A, Wu CH et al (2004) UniProt: the Universal Protein knowledgebase. Nucleic Acids Res 32:D115–D119

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Craig R, Cortens JP, Beavis RC (2004) Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 3:1234–1242

    Article  CAS  PubMed  Google Scholar 

  17. Lane L, Argoud-Puy G, Britan A et al (2012) neXtProt: a knowledge platform for human proteins. Nucleic Acids Res 40:D76–D83

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Vizcaino JA, Mueller M, Hermjakob H et al (2009) Charting online OMICS resources: a navigational chart for clinical researchers. Proteomics Clin Appl 3:18–29

    Article  CAS  PubMed  Google Scholar 

  19. Hahne H, Moghaddas Gholami A, Kuster B (2012) Discovery of O-GlcNAc-modified proteins in published large-scale proteome data. Mol Cell Proteomics 11:843–850

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Matic I, Ahel I, Hay RT (2012) Reanalysis of phosphoproteomics data uncovers ADP-ribosylation sites. Nat Methods 9:771–772

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Martens L, Nesvizhskii AI, Hermjakob H et al (2005) Do we want our data raw? Including binary mass spectrometry data in public proteomics data repositories. Proteomics 5: 3501–3505

    Article  CAS  PubMed  Google Scholar 

  22. Fannes T, Vandermarliere E, Schietgat L et al (2013) Predicting tryptic cleavage from proteomics data using decision tree ensembles. J Proteome Res 12:2253–2259

    Article  CAS  PubMed  Google Scholar 

  23. Vandermarliere E, Martens L (2013) Protein structure as a means to triage proposed PTM sites. Proteomics 13:1028–1035

    Article  CAS  PubMed  Google Scholar 

  24. Desiere F, Deutsch EW, King NL et al (2006) The PeptideAtlas project. Nucleic Acids Res 34:D655–D658

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Martens L, Hermjakob H, Jones P et al (2005) PRIDE: the proteomics identifications database. Proteomics 5:3537–3545

    Article  CAS  PubMed  Google Scholar 

  26. Vizcaino JA, Foster JM, Martens L (2010) Proteomics data repositories: providing a safe haven for your data and acting as a springboard for further research. J Proteomics 73:2136–2146

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Wang R, Fabregat A, Rios D et al (2012) PRIDE Inspector: a tool to visualize and validate MS proteomics data. Nat Biotechnol 30:135–137

    Article  PubMed Central  PubMed  Google Scholar 

  28. Barsnes H, Martens L (2013) Crowdsourcing in proteomics: public resources lead to better experiments. Amino Acids 44:1129–1137

    Article  CAS  PubMed  Google Scholar 

  29. Levin Y (2011) The role of statistical power analysis in quantitative proteomics. Proteomics 11:2565–2567

    Article  CAS  PubMed  Google Scholar 

  30. Oberg AL, Vitek O (2009) Statistical design of quantitative mass spectrometry-based proteomic experiments. J Proteome Res 8: 2144–2156

    Article  CAS  PubMed  Google Scholar 

  31. Karp NA, Lilley KS (2009) Investigating sample pooling strategies for DIGE experiments to address biological variability. Proteomics 9:388–397

    Article  CAS  PubMed  Google Scholar 

  32. Geiger T, Cox J, Ostasiewicz P et al (2010) Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7:383–385

    Article  CAS  PubMed  Google Scholar 

  33. Bantscheff M, Schirle M, Sweetman G et al (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389:1017–1031

    Article  CAS  PubMed  Google Scholar 

  34. Vaudel M, Sickmann A, Martens L (2010) Peptide and protein quantification: a map of the minefield. Proteomics 10:650–670

    Article  CAS  PubMed  Google Scholar 

  35. Domon B, Aebersold R (2010) Options and considerations when selecting a quantitative proteomics strategy. Nat Biotechnol 28:710–721

    Article  CAS  PubMed  Google Scholar 

  36. Vaudel M, Burkhart JM, Radau S et al (2012) Integral quantification accuracy estimation for reporter ion-based quantitative proteomics (iQuARI). J Proteome Res 11:5072–5080

    Article  CAS  PubMed  Google Scholar 

  37. Vaudel M, Burkhart JM, Breiter D et al (2012) A complex standard for protein identification, designed by evolution. J Proteome Res 11:5065–5071

    Article  CAS  PubMed  Google Scholar 

  38. Muth T, Benndorf D, Reichl U et al (2013) Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Mol Biosyst 9:578–585

    Article  CAS  PubMed  Google Scholar 

  39. Castellana NE, Payne SH, Shen Z et al (2008) Discovery and revision of Arabidopsis genes by proteogenomics. Proc Natl Acad Sci U S A 105:21034–21038

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Moruz L, Pichler P, Stranzl T et al (2013) Optimized nonlinear gradients for reversed-phase liquid chromatography in shotgun proteomics. Anal Chem 85:7777–7785

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Jenden DJ, Cho AK (1979) Selected ion monitoring in pharmacology. Biochem Pharmacol 28:705–713

    Article  CAS  PubMed  Google Scholar 

  42. Yost RA, Enke CG (1979) Triple quadrupole mass spectrometry for direct mixture analysis and structure elucidation. Anal Chem 51: 1251–1264

    Article  CAS  PubMed  Google Scholar 

  43. Purvine S, Eppel JT, Yi EC et al (2003) Shotgun collision-induced dissociation of peptides using a time of flight mass analyzer. Proteomics 3:847–850

    Article  CAS  PubMed  Google Scholar 

  44. Craig R, Cortens JP, Beavis RC (2005) The use of proteotypic peptide libraries for protein identification. Rapid Commun Mass Spectrom 19:1844–1850

    Article  CAS  PubMed  Google Scholar 

  45. Barsnes H, Eidhammer I, Martens L (2011) A global analysis of peptide fragmentation variability. Proteomics 11:1181–1188

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  47. Degroeve S, Martens L (2013) MS2PIP: a tool for MS/MS peak intensity prediction. Bioinformatics 29(24):3199–3203

    Article  CAS  PubMed  Google Scholar 

  48. Moruz L, Staes A, Foster JM et al (2012) Chromatographic retention time prediction for posttranslationally modified peptides. Proteomics 12:1151–1159

    Article  CAS  PubMed  Google Scholar 

  49. Nahnsen S, Kohlbacher O (2012) In silico design of targeted SRM-based experiments. BMC Bioinformatics 13 Suppl 16:S8

    Google Scholar 

  50. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372

    Article  CAS  PubMed  Google Scholar 

  51. Orchard S, Jones P, Taylor C et al (2007) Proteomic data exchange and storage: the need for common standards and public repositories. Methods Mol Biol 367:261–270

    CAS  PubMed  Google Scholar 

  52. Kinsinger CR, Apffel J, Baker M et al (2012) Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles). J Proteome Res 11:1412–1419

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Kinsinger CR, Apffel J, Baker M et al (2012) Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles). Proteomics 12:11–20

    Article  CAS  PubMed  Google Scholar 

  54. Kinsinger CR, Apffel J, Baker M et al (2011) Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles). Proteomics Clin Appl 5:580–589

    Article  CAS  PubMed  Google Scholar 

  55. Kinsinger CR, Apffel J, Baker M et al (2011) Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles). Mol Cell Proteomics 10:O111.015446

    Article  PubMed Central  PubMed  Google Scholar 

  56. Martens L (2011) Data management in mass spectrometry-based proteomics. Methods Mol Biol 728:321–332

    Article  CAS  PubMed  Google Scholar 

  57. Hakkinen J, Vincic G, Mansson O et al (2009) The proteios software environment: an extensible multiuser platform for management and analysis of proteomics data. J Proteome Res 8:3037–3043

    Article  CAS  PubMed  Google Scholar 

  58. Piggee C (2008) LIMS and the art of MS proteomics. Anal Chem 80:4801–4806

    Article  CAS  PubMed  Google Scholar 

  59. Stephan C, Kohl M, Turewicz M et al (2010) Using laboratory information management systems as central part of a proteomics data workflow. Proteomics 10:1230–1249

    Article  CAS  PubMed  Google Scholar 

  60. Weisser H, Nahnsen S, Grossmann J et al (2013) An automated pipeline for high-throughput label-free quantitative proteomics. J Proteome Res 12(4):1628–1644

    Article  CAS  PubMed  Google Scholar 

  61. Lange E, Gropl C, Reinert K et al (2006) High-accuracy peak picking of proteomics data using wavelet techniques. Pac Symp Biocomput 243–254

    Google Scholar 

  62. Martin SF, Falkenberg H, Dyrlund TF et al (2013) PROTEINCHALLENGE: crowd sourcing in proteomics analysis and software development. J Proteomics 88:41–46

    Article  CAS  PubMed  Google Scholar 

  63. Keller A, Eng J, Zhang N et al (2005) A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Syst Biol 1:2005.0017

    Article  PubMed Central  PubMed  Google Scholar 

  64. Sturm M, Bertsch A, Gropl C et al (2008) OpenMS—an open-source software framework for mass spectrometry. BMC Bioinformatics 9:163

    Article  PubMed Central  PubMed  Google Scholar 

  65. Kessner D, Chambers M, Burke R et al (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24:2534–2536

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  66. Junker J, Bielow C, Bertsch A et al (2012) TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data. J Proteome Res 11:3914–3920

    Article  CAS  PubMed  Google Scholar 

  67. Elias JE, Gygi SP (2010) Target-decoy search strategy for mass spectrometry-based proteomics. Methods Mol Biol 604:55–71

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  68. Everett LJ, Bierl C, Master SR (2010) Unbiased statistical analysis for multi-stage proteomic search strategies. J Proteome Res 9:700–707

    Article  CAS  PubMed  Google Scholar 

  69. Ivanov AR, Colangelo CM, Dufresne CP et al (2013) Interlaboratory studies and initiatives developing standards for proteomics. Proteomics 13:904–909

    Article  CAS  PubMed  Google Scholar 

  70. Martens L, Vizcaino JA, Banks R (2011) Quality control in proteomics. Proteomics 11:1015–1016

    Article  CAS  PubMed  Google Scholar 

  71. Tabb DL (2013) Quality assessment for clinical proteomics. Clin Biochem 46:411–420

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  72. Martens L (2013) Bringing proteomics into the clinic: the need for the field to finally take itself seriously. Proteomics Clin Appl 7: 388–391

    Article  CAS  PubMed  Google Scholar 

  73. Burkhart JM, Premsler T, Sickmann A (2011) Quality control of nano-LC-MS systems using stable isotope-coded peptides. Proteomics 11: 1049–1057

    Article  CAS  PubMed  Google Scholar 

  74. Staes A, Vandenbussche J, Demol H et al (2013) Asn3, a reliable, robust and universal lock mass for improved accuracy in LC-MS and LC-MS/MS. Anal Chem 85(22):11054–11060

    Article  CAS  PubMed  Google Scholar 

  75. Cote RG, Reisinger F, Martens L (2010) jmzML, an open-source Java API for mzML, the PSI standard for MS data. Proteomics 10:1332–1335

    Article  CAS  PubMed  Google Scholar 

  76. Sturm M, Kohlbacher O (2009) TOPPView: an open-source viewer for mass spectrometry data. J Proteome Res 8:3760–3763

    Article  CAS  PubMed  Google Scholar 

  77. Pichler P, Mazanek M, Dusberger F et al (2012) SIMPATIQCO: a server-based software suite which facilitates monitoring the time course of LC-MS performance metrics on Orbitrap instruments. J Proteome Res 11:5540–5547

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  78. Gonnelli G, Hulstaert N, Degroeve S et al (2012) Towards a human proteomics atlas. Anal Bioanal Chem 404:1069–1077

    Article  CAS  PubMed  Google Scholar 

  79. Foster JM, Degroeve S, Gatto L et al (2011) A posteriori quality control for the curation and reuse of public proteomics data. Proteomics 11:2182–2194

    Article  CAS  PubMed  Google Scholar 

  80. Domon B, Aebersold R (2006) Mass spectrometry and protein analysis. Science 312:212–217

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  82. Bastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. https://gephi.org/

  83. Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics 4:1419–1440

    Article  CAS  PubMed  Google Scholar 

  84. Vaudel M, Sickmann A, Martens L (2013) Introduction to opportunities and pitfalls in functional mass spectrometry based proteomics. Biochim Biophys Acta 1844(1 Pt A):12–20

    PubMed  Google Scholar 

  85. Flicek P, Amode MR, Barrell D et al (2011) Ensembl 2011. Nucleic Acids Res 39: D800–D806

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  86. Cox J, Mann M (2012) 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics 13 Suppl 16:S12

    Google Scholar 

  87. Kasprzyk A, Keefe D, Smedley D et al (2004) EnsMart: a generic system for fast and flexible access to biological data. Genome Res 14: 160–169

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  88. Kasprzyk A (2011) BioMart: driving a paradigm change in biological data management. Database (Oxford) 2011:bar049

    Article  Google Scholar 

  89. Smedley D, Haider S, Ballester B et al (2009) BioMart—biological queries made easy. BMC Genomics 10:22

    Article  PubMed Central  PubMed  Google Scholar 

  90. Villaveces JM, Jimenez RC, Garcia LJ et al (2011) Dasty3, a WEB framework for DAS. Bioinformatics 27:2616–2617

    CAS  PubMed Central  PubMed  Google Scholar 

  91. Barsnes H, Vizcaino JA, Eidhammer I et al (2009) PRIDE Converter: making proteomics data-sharing easy. Nat Biotechnol 27: 598–599

    Article  CAS  PubMed  Google Scholar 

  92. Cote RG, Griss J, Dianes JA et al (2012) The PRoteomics IDEntification (PRIDE) Converter 2 framework: an improved suite of tools to facilitate data submission to the PRIDE database and the ProteomeXchange consortium. Mol Cell Proteomics 11: 1682–1689

    Article  PubMed Central  PubMed  Google Scholar 

  93. Martens L, Palazzi LM, Hermjakob H (2008) Data standards and controlled vocabularies for proteomics. Methods Mol Biol 484:279–286

    Article  CAS  PubMed  Google Scholar 

  94. Cote R, Reisinger F, Martens L et al (2010) The Ontology Lookup Service: bigger and better. Nucleic Acids Res 38:W155–W160

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  95. Barsnes H, Cote RG, Eidhammer I et al (2010) OLS dialog: an open-source front end to the ontology lookup service. BMC Bioinformatics 11:34

    Article  PubMed Central  PubMed  Google Scholar 

  96. Klie S, Martens L, Vizcaino JA et al (2008) Analyzing large-scale proteomics projects with latent semantic indexing. J Proteome Res 7:182–191

    Article  CAS  PubMed  Google Scholar 

  97. (2013) In need of an upgrade. Nat Biotechnol 31:857. doi: 10.1038/nbt.2717

    Google Scholar 

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Acknowledgements

H.B. is supported by the Research Council of Norway. L.M. acknowledges the support of Ghent University (Multidisciplinary Research Partnership “Bioinformatics: from nucleotides to networks”), the PRIME-XS project, grant agreement number 262067, and the “ProteomeXchange” project, grant agreement number 260558, both funded by the European Union 7th Framework Program. The authors have no competing financial or commercial interests.

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Vaudel, M., Barsnes, H., Martens, L., Berven, F.S. (2014). Bioinformatics for Proteomics: Opportunities at the Interface Between the Scientists, Their Experiments, and the Community. In: Martins-de-Souza, D. (eds) Shotgun Proteomics. Methods in Molecular Biology, vol 1156. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0685-7_16

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