As we mentioned before, we want to focus this manuscript on peptide-level intensity analyses because previously MaxQuant has been benchmarked with protein-level data. We analyzed the dataset PXD007145 with quantms and use Proteus and MSstats using only two unique peptides per protein (to decrease the level of variability introduced by shared peptides). Figure shows the CV distribution in at least 50% replicates for different tools and normalization methods. The MSstats package achieved a lower average CV of 16.5% across conditions than Proteus (average of 21.2%). For statistical analysis, we plotted the log ratios against the log of the protein intensity (Supplementary Figure ), which can be used as a proxy for protein abundance. In all cases, human proteins formed distinct clouds, but with different degrees of overlap. Proteus got more discrete log2 fold change values and their log ratios adopted a wider distribution with pronounced overlap of human, yeast low and high fold changes proteins (Supplementary Figure ). The widths of the distributions and the degree of overlap (Supplementary Figure ) suggested that MSstats usually performed better not only in generating narrow distributions but also by most accurately recapitulating the expected fold change.