Abstract Background: To effectively safeguard the food-allergic population and support compliance with food-labeling regulations, the food industry and regulatory agencies require reliable methods for food allergen detection and quantification. MS-based detection of food allergens relies on the systematic identification of robust and selective target peptide markers. The selection of proteotypic peptide markers, however, relies on the availability of high-quality protein sequence information, a bottleneck for the analysis of many plant-based proteomes. Method: In this work, data were compiled for reference tree nut ingredients and evaluated using a parsimony-driven global proteomics workflow. Results: The utility of supplementing existing incomplete protein sequence databases with translated genomic sequencing data was evaluated for English walnut and provided enhanced selection of candidate peptide markers and differentiation between closely related species. Highlights: Future improvements of protein databases and release of genomics-derived sequences are expected to facilitate the development of robust and harmonized LC–tandem MS-based methods for food allergen detection.