Abstract
Multiple reaction monitoring (MRM) is a mass spectrometric method to quantify a specified set of proteins. In this paper, we identify a problem at the core of MRM peptide quantification accuracy. In mathematical terms, the problem is to find for a given matrix a submatrix with best condition number. We show this problem to be NP-hard, and we propose a greedy heuristic. Our numerical experiments show this heuristic to be orders of magnitude better than currently used methods.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Gygi, S., Rist, B., Gerber, S., Turecek, F., Gelb, M., Aebersold, R.: Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nature Biotechnology 17, 994–999 (1999)
Ross, P., Huang, Y., Marchese, J., et al.: Multiplexed Protein Quantitation in Saccharomyces cerevisiae Using Amine-reactive Isobaric Tagging Reagents. Molecular & Cellular Proteomics 3(12), 1154–1169 (2004)
Listgarten, J., Emili, A.: Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry. Molecular & Cellular Proteomics 4(4), 419–434 (2005)
Fischer, B., Grossmann, J., Roth, V., Gruissem, W., Baginsky, S., Buhmann, J.: Semi-supervised LC/MS alignment for differential proteomics. Bioinformatics 22(14) (2006)
Fischer, B., Roth, V., Buhmann, J.: Time-series alignment by non-negative multiple generalized canonical correlation analysis, feedback (2008)
Šrámek, R., Fischer, B., Vicari, E., Widmayer, P.: Optimal Transitions for Targeted Protein Quantification: Best Conditioned Submatrix Selection, http://www.pw.inf.ethz.ch
Stoer, J., Bulirsch, R.: Introduction to Numerical Analysis, 3rd edn. Springer, Heidelberg (2002)
Bartholdi, J.: A Good Submatrix is Hard to Find. Operations Research Letters 1(5), 190–193 (1982)
Lovász, L., Saks, M., Schrijver, A.: Orthogonal Representations and Connectivity of Graphs. Linear Algebra Applications 114-115, 439–454 (1989)
Karp, R.: Reducibility Among Combinatorial Problems. Complexity of Computer Computations, 85–103 (1972)
Fischer, B., Roth, V., Roos, F., Grossmann, J., Baginsky, S., Widmayer, P., Gruissem, W., Buhmann, J.: NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing. Analytical Chemistry 77(22), 7265–7273 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Šrámek, R., Fischer, B., Vicari, E., Widmayer, P. (2009). Optimal Transitions for Targeted Protein Quantification: Best Conditioned Submatrix Selection. In: Ngo, H.Q. (eds) Computing and Combinatorics. COCOON 2009. Lecture Notes in Computer Science, vol 5609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02882-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-02882-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02881-6
Online ISBN: 978-3-642-02882-3
eBook Packages: Computer ScienceComputer Science (R0)