Algorithms that can robustly identify post-translational protein modifications from mass spectrometry data are needed for data-mining and furthering biological interpretations. In this study, we determined that a mass-based alignment algorithm (OpenSea) for de novo sequencing results could identify post-translationally modified peptides in a high-throughput environment. A complex digest of proteins from human cataractous lens, a tissue containing a high abundance of modified proteins, was analyzed using two-dimensional liquid chromatography, and data was collected on both high and low mass accuracy instruments. The data were analyzed using automated de novo sequencing followed by OpenSea mass-based sequence alignment. A total of 80 modifications were detected, 36 of which were previously unreported in the lens. This demonstrates the potential to identify large numbers of known and previously unknown protein modifications in a given tissue using automated data processing algorithms such as OpenSea.