Abstract: A method identifies glycopeptides in a sample. The method includes converting a mass spectrum of MS1 precursors of the sample into a plurality of nodes in a graph, each node corresponding to one mass and one retention time of a glycopeptide to be identified in the sample; calculating differences in the mass and/or retention time between all combinations of pairs of the nodes; generating a graph theoretic network of the nodes; and predicting compositions of the glycopeptides in the sample based on the graph theoretic network of the nodes so as to identify the glycopeptides.

Abstract: A method for identifying an unknown biological sample (e.g. a glycan, an antibody, a metabolite) is disclosed. The method comprises: receiving more than two sample measurements for the unknown biological sample, calculating a sample point in a two-dimensional plot from the more than two sample measurements for the unknown biological sample and identifying the unknown biological sample by comparing the sample point against the plurality of reference points in the two-dimensional plot. The two-dimensional plot includes a plurality of stored reference points corresponding to respective known biological compounds. Each reference point is calculated from a plurality of reference measurements for more than two attributes of the corresponding known biological compound (e.g. by performing principal component analysis on the plurality of reference measurements), with each attribute being different from another attribute. Each reference measurement may be obtained experimentally (e.g.

Abstract: A method identifies glycopeptides in a sample. The method includes converting a mass spectrum of MS1 precursors of the sample into a plurality of nodes in a graph, each node corresponding to one mass and one retention time of a glycopeptide to be identified in the sample; calculating differences in the mass and/or retention time between all combinations of pairs of the nodes; generating a graph theoretic network of the nodes; and predicting compositions of the glycopeptides in the sample based on the graph theoretic network of the nodes so as to identify the glycopeptides.