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GlycoDeNovo2: An Improved MS/MS-Based De Novo Glycan Topology Reconstruction Algorithm.

Zizhang ChenJuan WeiYang TangCheng LinCatherine E CostelloPengyu Hong
Published in: Journal of the American Society for Mass Spectrometry (2022)
Glycan structure identification is essential to understanding the roles of glycans in various biological processes. Previously, we developed GlycoDeNovo, a de novo algorithm for reconstructing glycan topologies from tandem mass spectra (MS/MS). In this work, we introduce GlycoDeNovo2 that contains two major improvements to GlycoDeNovo. First, we use the precursor mass measured for a peak that likely corresponds to a glycan to determine its potential compositions, which are used to constrain the search space, enable parallel computation, and hence speed up topology reconstruction. Second, we developed a procedure to calculate the empirical p -value of a reconstructed topology candidate. Experimental results are provided to demonstrate the effectiveness of GlycoDeNovo2.
Keyphrases
  • ms ms
  • cell surface
  • machine learning
  • deep learning
  • randomized controlled trial
  • systematic review
  • liquid chromatography tandem mass spectrometry
  • simultaneous determination