GlycoDeNovo2: An Improved MS/MS-Based <i>De Novo</i> Glycan Topology Reconstruction Algorithm
Zizhang Chen, Juan Wei, Ting Yang, Cheng Lin, Catherine E. Costello, Pengyu Hong
Abstract
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.