De Novo Sequencing Assisted Approach for Characterizing Mixture MS/MS Spectra

peer-reviewed · IEEE Transactions on NanoBioscience · 2016

peer-reviewed · IEEE Transactions on NanoBioscience · 2016. Yi Liu (Beijing) et al. Extensive research has been conducted for the computational analysis of mass spectrometry based proteomics…
Date 2016-03-01
Type peer-reviewed
Venue IEEE Transactions on NanoBioscience
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Contribution algorithm
DOI 10.1109/tnb.2016.2519841
Citations (OpenAlex) 2

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

  1. Yi Liu (Beijing) · Beijing Institute of Lifeomics, International Academy of Phronesis Medicine (Guangdong), Peking Union Medical College Hospital
  2. Yi Liu (Western Ontario) · University of Western Ontario
  3. Yi Liu · University of Western Ontario
  4. Weiping Sun · Bioinformatics Solutions Inc.
  5. Julius John · University of Western Ontario
  6. Gilles Lajoie · University of Western Ontario
  7. Bin Ma · Rapid Novor Inc., University of Waterloo, University of Western Ontario
  8. Kaizhong Zhang · University of Western Ontario

Methods and tools

Cites (8)

Seen in the charts

Back to the full map

Back to top