Computational quantification of peptides from LC-MS data

Journal of Computational Biology

Volumn 15, Issue 7, 2008, Pages 685-704

  Schulz Trieglaff, Ole ^a   Hussong, Rene ^b   Gröpl, Clemens ^a   Leinenbach, Andreas ^b   Hildebrandt, Andreas ^b   Huber, Christian ^b   Reinert, Knut ^a  

^a FREIE UNIVERSITÄT BERLIN   (Germany)

^b SAARLAND UNIVERSITY   (Germany)

Author keywords

Computational mass spectrometry; Liquid chromatography mass spectrometry; Quantification; Wavelets

Indexed keywords

AMINO ACID; MYOGLOBIN; PEPTIDE DERIVATIVE;

AMINO ACID COMPOSITION; BINDING AFFINITY; CONFERENCE PAPER; DNA FINGERPRINTING; GENE SEQUENCE; HUMAN; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; MEDICAL INFORMATION; NONHUMAN; PRIORITY JOURNAL; PROTEIN FUNCTION; PROTEIN PROCESSING; SCORING SYSTEM; SIGNAL TRANSDUCTION;

ALGORITHMS; ANIMALS; CHROMATOGRAPHY, LIQUID; HALOBACTERIUM; HUMANS; MASS SPECTROMETRY; MYOGLOBIN; PEPTIDES; REGRESSION ANALYSIS; SOFTWARE;

EID: 51349105893     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2007.0117     Document Type: Conference Paper

References (30)

1. Bellew, M., Coram, M., Fitzgibbon, M., et al. 2006. A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS. Bioinformatics 22, 1902-1909.
2. Benjamini, Y., and Hochberg, Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B Methodol. 57, 289-300.
3. Marco, V.B., and Bombi, G.G. 2001. Mathematical functions for the representation of chromatographic peaks. J. Chromatogr. A 931, 1-30.
4. Gröpl, C., Lange, E., Reinert, K., et al. 2005. Algorithms for the automated absolute quantification of diagnostic Markers in complex proteomics samples. Lect. Notes Bioinform. 3695, 151-163.
5. Horn, D.M., Zubarev, R.A., and McLafferty, F.W. 2000. Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules. J. Am. Soc. Mass Spectrom. 11, 320-332.
6. Katajamaa, M., and Orešič, M. 2005. Processing methods for differential analysis of LC/MS profile data. BMC Bioinform. 6, 179.
7. Katajamaa, M., Miettinen, J., and Orešič, M. 2006. MZmine: toolbox for processing and visualization of mass spectrometry-based molecular profile data. Bioinformatics 22, 634-636.
8. Kohlbacher, O., Reinert, K., Gröpl, C., et al. 2007. TOPP - the OpenMS proteomics pipeline. Bioinformatics 23, e191-e197.
9. Kubinyi, H. 1991. Calculation of isotope distributions in mass spectrometry. A trivial solution for a non-trivial problem. Anal. Chim. Acta 247, 107-109.
10. Lange, E., Gröpl, C., Schulz-Trieglaff, O., et al. 2007. A geometric approach for the alignment of liquid chromatography-mass spectrometry data. Bioinformatics 23, i273-281.
11. Leptos, K.C., Sarracino, D.A., Jaffe, J.D., et al. 2006. MapQuant: open-source software for large-scale protein quantification. Proteomics 6, 1770-1782.
12. MacCoss, M., and Matthews, D.E. 2005. Quantitative MS for proteomics: teaching a new dog old tricks. Anal. Chem. 77, 294A-302A.
13. Mann, M., and Aebersold, R. 2003. Mass spectrometry-based proteomics. Nature 422, 198-207.
14. Mayr, B.M., Kohlbacher, O., Reinert, K., et al. 2006. Absolute myoglobin quantitation in serum by combining twodimensional liquid chromatography- electrospray ionization mass spectrometry and novel data analysis algorithms. J. Proteome Res. 5, 414-421.
15. Ong, S.-E., Blagoev, B., Kratchmarova, I., et al. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376-386.
16. Ong, S.-E., and Mann, M. 2005. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252-262.
17. Piening, B., Wang, P., Bangur, C., et al. 2006. Quality control metrics for LC-MS feature detection tools demonstrated on saccharomyces cerevisiae proteomic profiles. J. Proteome Res. 5, 1527-1534.
18. Prakash, A., Mallick, P., Whiteaker, J., et al. 2006. Signal maps for mass spectrometry-based comparative proteomics. Mol. Cell Proteomics 5, 423-432.
19. Prince, J., and Marcotte, E. 2006. Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Anal. Chem. 78, 6140-6152.
20. Schley, C., Swart, R., and Huber, C. 2006. Capillary scale monolithic trap column for desalting and preconcentration of peptides and proteins in one- and two-dimensional separations. J. Chromatogr. A 1136, 210-220.
21. Schulz-Trieglaff, O., Hussong, R., Gröpl, C., et al. 2007. A fast and accurate algorithm for the quantification of peptides from LC-MS data. Lect. Notes Comput. Sci. 4453, 473-487.
22. Senko, M., Beu, S., and McLafferty, F. 1995. Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions. J. Am. Soc. Mass Spectrom. 6, 229-233.
23. Shiio, Y., and Aebersold, R. 2006. Quantitative proteome analysis using isotope-coded affinity tags and mass spectrometry. Nat. Protocols 1, 139-145.
24. Smith, C., Want, E., O'Maille, G., et al. 2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779-787.
25. Tan, C.S., Ploner, A., Quandt, A., et al. 2006. Finding regions of significance in seldi measurements for identifying protein biomarkers. Bioinformatics 22, 1515-1523.
26. Toll, H., Wintringer, R., Schweiger-Hufnagel, U., et al. 2005. Comparing monolithic and microparticular capillary columns for the separation and analysis of peptide mixtures by liquid chromatography-mass spectrometry. J. Separation Sci. 28, 1666-1674.
27. Wang, W., Zhou, H., Lin, H., et al. 2003. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal. Chem. 75, 4818-4826.
28. Whitehead, K., Kish, A., Pan, M., et al. 2006. An integrated systems approach for understanding cellular responses to gamma radiation. Mol. Syst. Biol. 2.
29. Xue, X., Wu, S., Wang, Z., et al. 2006. Protein probabilities in shotgun proteomics: evaluating different estimation methods using a semi-random sampling model. Proteomics 6, 6134-6145.
30. Zhang, Z., and Marshall, A.G. 1998. A universal algorithm for fast and automated charge state deconvolution of electrospray mass-to-charge ratio spectra. J. Am. Soc. Mass Spectrom. 9, 225-233.