Quantitative proteomics can provide rich information on changes in biological functions and processes. However, its accuracy is affected by the inherent information degeneration found in bottom-up proteomics. Therefore, the precise protein inference from identified peptides can be mistaken since an ad hoc rule is used for generating a list of protein groups that depends on both the sample type and the sampling depth. Herein, we propose an alternative approach for examining quantitative proteomic data which is peptide-centric instead of protein-centric. We discuss the feasibility of the peptide-centric approach which was tested on several quantitative proteomic data sets. We show that peptide-centric quantification has several advantages over protein level analysis: (1) it is more sensitive for sample segregation, (2) it avoids the issues associated with protein inference, and (3) it can retrieve significant peptides lost in protein-centric quantification for further downstream analysis.