title: De Novo Sequencing Assisted Approach for Characterizing Mixture MS/MS Spectra.
abstract: Extensive research has been conducted for the computational analysis of mass spectrometry based proteomics data. However, there are still remaining challenges, among which, one particular challenge is the low identification rate of the collected spectral data. A specific contributing factor is the existence of mixture spectra in the collected MS/MS spectra which are generated by the concurrent fragmentation of multiple precursors in one sequencing attempt. The quite frequently observed mixture spectra necessitates the development of effective computational approaches to characterize those non-conventional spectral data. In this research, we proposed an approach for matching the query mixture spectra with a pair of peptide sequences acquired from the protein database by incorporating a special de novo assisted filtration strategy. The experiment results on two different datasets of MS/MS spectra containing mixed ion fragments from multiple peptides demonstrated the efficiency of the integrated filtration strategy in reducing examination space and verified the effectiveness of the proposed matching scheme as well.
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.forename: Weiping
authors.initials: W
authors.lastname: Sun
authors.forename: Julius
authors.initials: J
authors.lastname: John
authors.forename: Gilles
authors.initials: G
authors.lastname: Lajoie
authors.forename: Bin
authors.initials: B
authors.lastname: Ma
authors.forename: Kaizhong
authors.initials: K
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D030562:Databases, Protein; D010446:Peptide Fragments / Q000032:analysis / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D053719:Tandem Mass Spectrometry / Q000145:classification* / Q000379:methods*
chemical_list: D010446:Peptide Fragments