State-of-the-art mass spectrometers combined with modern bioinformatics algorithms for peptide-to-spectrum matching (PSM) with robust statistical scoring allow for more variable features (i.e., post-translational modifications) being reliably identified from (tandem-) mass spectrometry data, often without the need for biochemical enrichment. Semi-specific proteome searches, that enforces a theoretical enzymatic digestion to solely the N- or C-terminal end, allow to identify native protein termini or those arising from endogenous proteolytic activity (also referred to ‘neo-N-termini’ analysis or ‘N-terminomics’. Nevertheless, deriving biological meaning from these search outputs can be challenging in terms of data mining and analysis. Thus, we introduce Fragterminomics, a data analysis approach for the (1) annotation of peptides according to their enzymatic cleavage specificity, (2) differential abundance and enrichment analysis of N-terminal sequence patterns, (3) visualization of neo-N-termini location, and (4) mapping neo-N-termini to known protein processing features. We illustrate the use of Fragterminomics by applying it to tandem mass tag (TMT)-based proteomics data of a mouse model of polycystic kidney disease and assess the semi-specific searches for biological interpretation of cleavage events and the variable contribution of proteolytic products to general protein abundance. The Fragterminomics approach and example data are available as an R package at https://github.com/MiguelCos/Fragterminomics.