Fig. 4: Workflow of library-based virtual match-between-run quantification enables the identification and quantification of glycopeptides without MS2 spectra in new acquired data.

a Construction workflow of library used in library-base identification and quantification by GlyPep-Quant. b Isotopic distributions and glycopeptide precursor information used in this example. Source data are provided as a Source Data file. c Extracted elution profiles in the whole retention time window of the precursor in (b). Source data are provided as a Source Data file. d Scores, DP and match indexes matrices used in the MS1 feature cluster matching algorithm, and the best matching path selected by the matching algorithm. The cluster contains MS1 features I, II, and III, their corresponding matched profiles are profile 3, 5, and 6. Since feature II-profile 5 and III-profile 5 have the highest score among feature II and III matches, if FC algorithm is not utilized, then feature III-profile 6 cannot be obtained for its lower score. However, the score of feature III-profile 6 match (0.896) is also high, and more importantly, profile 6 is the one which can be extracted based on GPSM identification, meaning it be correct profile. These results show that FC algorithm can acquire more accurate and comprehensive quantification, minimizing quantitative loss caused by precursors of the same m/z eluting in different time regions.