Proteins carry out the majority of functions in a living cell. Yet proteins are often subject to naturally occurring modifications of their basic structure, which happen after the protein has been synthetized in the cell. These modifications are termed post-translational modifications (PTMs) and can have substantial effects on protein function and activity. Therefore, these PTMs are highly relevant to our understanding of the cell in health and disease. Several bioinformatics tools (including our in-house software tool ionbot) have already been developed to detect these PTMs on the proteome, the entire set of proteins that can be expressed by cells at a certain moment. However, as there are currently no reliable methods to quantify the differential occurrence of PTM-carrying proteins across samples, this information cannot yet be fully exploited. I here therefore propose to create the statistical means to obtain a proteome-wide quantitative view of PTMs. These novel statistical means can, after fine-tuning, be implemented in the existing ionbot tool. This will add considerable commercial value to this powerful tool, as it will extend its existing PTM identification capabilities with novel and powerful PTM quantification capabilities. This is particularly relevant in light of current plans in the group to create an ionbot-centered start-up company.