Abstract
In the past decade, extensive research has been conducted for the computational analysis of mass spectrometry based proteomics data. Yet, there are still remaining challenges, among which, one particular challenge is that the identification rate of the MS/MS spectra collected is rather low. One significant reason that contributes to this situation is the concurrent fragmentation of multiple precursors in a single MS/MS spectrum. Nearly all the mainstream computational methods take the assumption that the acquired spectra come from a single precursor, thus they are not suitable for the identification of mixture spectra. In this research, we formulated the mixture spectra de novo sequencing problem mathematically, and proposed a dynamic programming algorithm for the problem. Experiment shows that our proposed algorithm can serve as a complimentary method for the identification of mixture spectra.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fenn, J.B., Mann, M., Meng, C.K., Wong, S.F., et al.: Electrospray Ionization for Mass Spectrometry of Large Biomolecules. Science 246(4926), 64–71 (1989)
Peng, J., Elias, J.E., et al.: Evaluation of Multidimensional Chromatography Coupled with Tandem Mass Spectrometry(LC/LC-MS/MS) for Large-scale Protein Analysis: the Yeast Proteome. J. Proteome Res. 2(1), 43–50 (2003)
Cottrell, J., et al.: Probability-based Protein Identification by Searching Sequence Database using Mass Spectrometry Data. Electrophoresis 20(18), 3551–3567 (1999)
Ma, B., et al.: PEAKS: Powerful Software for Peptide De Novo Sequencing by Tandem Mass Spectrometry. Rapid Commun. Mass Spectrom. 17(20), 2337–2342 (2003)
Ma, B., Zhang, K., Liang, C.: An Effective Algorithm for Peptide De Novo Sequencing from MS/MS Spectra. J. Comput. Syst. Sci. 70(3), 418–430 (2005)
Frank, A., Pevzner, P.: Pepnovo: De Novo Peptide Sequencing via Probabilistic Network Modeling. Anal. Chem. 77(4), 964–973 (2005)
Alves, G., Ogurtsov, A.Y., Kwok, S., Wu, W.W., Wang, G., et al.: Detection of Co-eluted Peptides using Database Search Methods. Biol. Direct. 3(27) (2008)
Houel, S., Abernathy, R., Rengariathan, K., Meyer-Arendt, K., et al.: Quantifying the Impact of Chimera MS/MS Spectra on Peptide Identification in Large-scale Proteomics Studies. J. Proteome Res. 9(8), 4152–4160 (2010)
Venable, J., et al.: Automated Approach for Quantitative Analysis of Complex Peptide Mixtures from Tandem Mass Spectra. Nat. Methods 1(1), 39–45 (2004)
Zhang, N., et al.: ProbIDtree: An Automated Software Program Capable of Identifiying Multiple Peptides from a Single Collision-induced Dissociation Spectrum Collected by a Tandem Mass Spectrometer. Proteomics 5(16), 4096–4106 (2005)
Wang, J., Perez-Santiago, J., Katz, J.E., et al.: Peptide Identification from Mixture Tandem Mass Spectra. Mol. Cell. Proteomics 9(7), 1476–1485 (2010)
Wang, J., Bourne, P.E., Bandeira, N.: Peptide Identification by Database Search of Mixture Tandem Mass Spectra. Mol. Cell. Proteomics 10(12) (2011)
Zhang, J., et al.: PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification. Mol. Cell. Proteomics 11(4) (2012)
Frank, A., Savitski, M.M., et al.: De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry. J. Proteome Res. 6(1), 114–123 (2007)
Vizcaino, J.A., et al.: The Proteomics Identification(PRIDE) Database and Associated Tools: Status in 2013. Nucleic Acids Res. 41(D1), D1063–D1069 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, Y., Ma, B., Zhang, K., Lajoie, G. (2014). An Effective Algorithm for Peptide de novo Sequencing from Mixture MS/MS Spectra. In: Basu, M., Pan, Y., Wang, J. (eds) Bioinformatics Research and Applications. ISBRA 2014. Lecture Notes in Computer Science(), vol 8492. Springer, Cham. https://doi.org/10.1007/978-3-319-08171-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-08171-7_12
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08170-0
Online ISBN: 978-3-319-08171-7
eBook Packages: Computer ScienceComputer Science (R0)