GlycoDeNovo -- an Efficient Algorithm for Accurate de novo Glycan Topology Reconstruction from Tandem Mass Spectras

A major challenge in glycomics is the determination of complex glycan structures that are essential for understanding their diverse roles in many biological processes. We have developed a novel efficient computational approach, named GlycoDeNovo, for accurate reconstruction of the glycan topologies from their tandem mass spectra. Given a spectrum, GlycoDeNovo first builds an interpretation-graph specifying how to interpret each peak using preceding interpreted peaks. It then reconstructs the topologies of peaks that contribute to interpreting the precursor ion. We theoretically prove that GlycoDeNovo is highly efficient. A major innovative feature added to GlycoDeNovo is a machine-learning based IonClassifier for effectively ranking candidate topologies. Our results showed that GlycoDeNovo is robust and accurate for topology reconstruction of glycans from their tandem mass spectra.

Funding: P41 GM104603 Mass Spectrometry Resource for Biology and Medicine; U01 CA221234 An open-source software suite for processing glycomics and glycoproteomics mass spectral data.

Download the source codes from GitHub.

Download the executable GlycanDeNovo here, uncompress the downloaded GlyCoDeNovo.rar to get GlycoDeNovo.jar, README.md and two example data files ("LewisB PM Na EED.20160410.txt" and "Man9_O18_PM.txt"). Follow the instructions in README.md to run GlycoDeNovo.jar.

** IonClassifier is not in the scope of this release.