Fig. 3: A MIR algorithm proposed as an optional function in pGlycoQuant.

a Overview of the MIR processing in pGlycoQuant. All glycopeptides are expected to be identified and quantified in one experiment, however, some of the glycopeptides could not be observed in the practical experiments. MIR could achieve more quantitative coverage of glycopeptides within one experiment. b The workflow of MIR. c–h Evaluation of the quantitative performance of MIR analysis on benchmarked N-glycopeptides. We used the data obtained from the experiments described in Supplementary Fig. 15 to validate the accuracy and sensitivity of MIR. The correlation of quantitative results reported by pGlycoQuant without MIR and with MIR for the subsistent standard glycopeptides, 0SA-GPs in mixture 1 (c) and 2SA-GPS in mixture 2 (e). MIR matching score reported by pGlycoQuant for mixture 1 (d) and mixture 2 (f). Box plot visualization of the fold change of the quantitation results of the 0SA-GPs (g) and 2SA-GPs (h) in a series of mixtures. Percent changes were calculated by dividing the intensity of each glycopeptide in each sample by the median intensity of this glycopeptide in all samples. The medians are indicated. The boxes indicate the interquartile ranges (IQRs), and the whiskers indicate 1.5 × IQR values; no outliers are shown. The gray dotted lines indicate the theoretical fold changes of the benchmarked N-glycopeptides (1:1:1 for 0SA-GPs in the mixtures of 1:5, 1:2, 1:1 (0SA-GPs:2SA-GPS), and 1:1:1 for 2SA-GPs in the mixtures of 1:1, 2:1, 5:1 (0SA-GPs:2SA-GPS)). Each sample in the above experiment was analyzed by LC-MS/MS with 2 replicates (c–f) or 4 replicates (g, h). Source data are provided as a Source Data file.