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Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

  • Magnus Palmblad
  • Davinia J. Mills
  • Laurence V. Bindschedler
  • Rainer Cramer
Articles

Abstract

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Keywords

Piecewise Linear Function Breakdown Point FTICR Mass Spectrometer Mascot Generic Format Hybrid Dataset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

13361_2011_181001835_MOESM1_ESM.doc (1.1 mb)
Supplementary material, approximately 1156 KB.

References

  1. 1.
    Li, X. J.; Yi, E. C.; Kemp, C. J.; Zhang, H.; Aebersold, R. A Software Suite for the Generation and Comparison of Peptide Arrays from Sets of Data Collected by Liquid Chromatography-Mass Spectrometry. Mol. Cell. Proteom. 2005, 4, 1328–1340.CrossRefGoogle Scholar
  2. 2.
    Syka, J. E.; Marto, J. A.; Bai, D. L.; Horning, S.; Senko, M. W.; Schwartz, J. C.; Ueberheide, B.; Garcia, B.; Busby, S.; Muratore, T.; Shabanowitz, J.; Hunt, D. F. Novel Linear Quadrupole Ion Trap/FT Mass Spectrometer: Performance Characterization and Use in the Comparative Analysis of Histone H3 Post-Translational Modifications. J Proteome Res. 2004, 3, 621–626.CrossRefGoogle Scholar
  3. 3.
    Bylund, D.; Danielsson, R.; Malmquist, G.; Markides, K. E. Chromatographic Alignment by Warping and Dynamic Programming as a Pre-Processing Tool for PARAFAC Modeling of Liquid Chromatography-Mass Spectrometry Data. J Chromatogr. A. 2002, 961, 237–244.CrossRefGoogle Scholar
  4. 4.
    Prakash, A.; Mallick, P.; Whiteaker, J.; Zhang, H.; Paulovich, A.; Flory, M.; Lee, H.; Aebersold, R.; Schwikowski, B. Signal Maps for Mass Spectrometry-Based Comparative Proteomics. Mol. Cell. Proteom. 2006, 5, 423–432.CrossRefGoogle Scholar
  5. 5.
    Prince, J. T.; Marcotte, E. M. Chromatographic Alignment of ESI-LC-MS Proteomics Data Sets by Ordered Bijective Interpolated Warping. Anal. Chem. 2006, 78, 6140–6152.CrossRefGoogle Scholar
  6. 6.
    van Nederkassel, A. M.; Daszykowski, M.; Eilers, P. H.; Heyden, Y. V. A Comparison of Three Algorithms for Chromatograms Alignment. J Chromatogr. A. 2006, 1118, 199–210.CrossRefGoogle Scholar
  7. 7.
    Sadygov, R. G.; Maroto, F. M.; Huhmer, A. F. ChromAlign: A Two-Step Algorithmic Procedure for Time Alignment of Three-Dimensional LC-MS Chromatographic Surfaces. Anal. Chem. 2006, 78, 8207–8217.CrossRefGoogle Scholar
  8. 8.
    Listgarten, J.; Emili, A. Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry. Mol. Cell. Proteom. 2005, 4, 419–434.CrossRefGoogle Scholar
  9. 9.
    Jaitly, N.; Monroe, M. E.; Petyuk, V. A.; Clauss, T. R.; Adkins, J. N.; Smith, R. D. Robust Algorithm for Alignment of Liquid Chromatography-Mass Spectrometry Analyses in an Accurate Mass and Time Tag Data Analysis Pipeline. Anal. Chem. 2006, 78, 7397–7409.CrossRefGoogle Scholar
  10. 10.
    Rousseeuw, P. J. Least Median of Squares Regression. J Am. Stat. Assoc. 1984, 79, 871–880.CrossRefGoogle Scholar
  11. 11.
    Pedrioli, P. G.; Eng, J. K.; Hubley, R.; Vogelzang, M.; Deutsch, E. W.; Raught, B.; Pratt, B.; Nilsson, E.; Angeletti, R. H.; Apweiler, R.; Cheung, K.; Costello, C. E.; Hermjakob, H.; Huang, S.; Julian, R. K.; Kapp, E.; McComb, M. E.; Oliver, S. G.; Omenn, G.; Paton, N. W.; Simpson, R.; Smith, R.; Taylor, C. F.; Zhu, W.; Aebersold, R. A Common Open Representation of Mass Spectrometry Data and Its Application to Proteomics Research. Nat. Biotechnol. 2004, 22, 1459–1466.CrossRefGoogle Scholar
  12. 12.
    Keller, A.; Eng, J.; Zhang, N.; Li, X. J.; Aebersold, R. A Uniform Proteomics MS/MS Analysis Platform Utilizing Open XML File Formats. Mol. Syst. Biol. 2005, 1, 2005.CrossRefGoogle Scholar
  13. 13.
    Andreev, V. P.; Li, L.; Rejtar, T.; Li, Q.; Ferry, J. G.; Karger, B. L. New Algorithm for 15N/14N Quantitation with LC-ESI-MS Using an LTQ-FT Mass Spectrometer. J. Proteome Res. 2006, 5, 2039–2045.CrossRefGoogle Scholar
  14. 14.
    Seattle Proteome Center (SPC)—Proteomics Tools. http://tools.proteomecenter.org/software.phpGoogle Scholar
  15. 15.
    Palmblad, M.; Bindschedler, L. V.; Gibson, T. M.; Cramer, R. Automatic Internal Calibration in Liquid Chromatography/Fourier Transform Ion Cyclotron Resonance Mass Spectrometry of Protein Digests. Rapid Commun. Mass Spectrom. 2006, 20, 3076–3080.CrossRefGoogle Scholar
  16. 16.
    Pruess, M.; Kersey, P.; Apweiler, R. The Integr8 Project—A Resource for Genomic and Proteomic Data. In Silico Biol. 2005, 5, 179–185.CrossRefGoogle Scholar
  17. 17.
    The Cygwin homepage. http://www.cygwin.com.Google Scholar
  18. 18.
    Hampel, F. R. A General Qualitative Definition of Robustness. Ann. Math. Stat. 1971, 42, 1887–1896.CrossRefGoogle Scholar
  19. 19.
    Xue, J.; Jorgensen, M.; Pihlgren, U.; Rask, L. The Myrosinase Gene Family in Arabidopsis thaliana: Gene Organization, Expression, and Evolution. Plant Mol. Biol. 1995, 27, 911–922.CrossRefGoogle Scholar
  20. 20.
    Kliebenstein, D. J.; Kroymann, J.; Mitchell-Olds, T. The Glucosinolate-Myrosinase System in an Ecological and Evolutionary Context. Curr. Opin. Plant Biol. 2005, 8, 264–271.CrossRefGoogle Scholar
  21. 21.
    Dixon, D. P.; Lapthorn, A.; Edwards, R. Plant Glutathione Transferases. Genome Biol. 2002, 3, Reviews 3004.Google Scholar
  22. 22.
    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. 2001, 19, 946–951.CrossRefGoogle Scholar
  23. 23.
    Parry, M. A.; Andralojc, P. J.; Mitchell, R. A.; Madgwick, P. J.; Keys, A. J. Manipulation of RuBisCo: The Amount, Activity, Function, and Regulation. J. Exp. Bot. 2003, 54, 1321–1333.CrossRefGoogle Scholar
  24. 24.
    Li, X. J.; Pedrioli, P. G.; Eng, J.; Martin, D.; Yi, E. C.; Lee, H.; Aebersold, R. A Tool to Visualize and Evaluate Data Obtained by Liquid Chromatography-Electrospray Ionization-Mass Spectrometry. Anal. Chem. 2004, 76, 3856–3860.CrossRefGoogle Scholar

Copyright information

© American Society for Mass Spectrometry 2007

Authors and Affiliations

  • Magnus Palmblad
    • 1
  • Davinia J. Mills
    • 1
  • Laurence V. Bindschedler
    • 1
  • Rainer Cramer
    • 1
    • 2
  1. 1.The BioCentreThe University of ReadingWhiteknights, ReadingUnited Kingdom
  2. 2.Department of ChemistryThe University of ReadingReadingUnited Kingdom

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