Degree
Doctor of Philosophy
Program
Computer Science
Supervisor
Kaizhong Zhang
Abstract
The large amount of data collected in an mass spectrometry experiment requires effective computational approaches for the automated analysis of those data. Though extensive research has been conducted for such purpose by the proteomics community, 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 frequently observed mixture spectra, which result from the concurrent fragmentation of multiple precursors in a single MS/MS spectrum. However, nearly all the mainstream computational methods still 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 focused on developing effective algorithms for the purpose of interpreting mixture tandem mass spectra, and our research work is mainly comprised of two components: de novo sequencing of mixture spectra and mixture spectra identification by database search. For the de novo sequencing approach, firstly we formulated the mixture spectra de novo sequencing problem mathematically, and proposed a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra datasets to verify the efficiency of the algorithm described in the research. For the database search identification, we proposed an approach for matching mixture tandem mass spectra with a pair of peptide sequences acquired from the protein sequence database by incorporating a special de novo assisted filtration strategy. Besides the filtration strategy, we also introduced in the research a method to give an reasonable estimation of the mixture coefficient which represents the relative abundance level of the co-sequenced precursors. The preliminary experimental results demonstrated the efficiency of the integrated filtration strategy and mixture coefficient estimating method in reducing examination space and also verified the effectiveness of the proposed matching scheme.
Recommended Citation
Liu, Yi, "Algorithms for Peptide Identification from Mixture Tandem Mass Spectra" (2015). Electronic Thesis and Dissertation Repository. 3151.
https://ir.lib.uwo.ca/etd/3151