Researchers at UCLA and the University of Toronto introduced moPepGen, an advanced computational algorithm improving detection of hidden genetic mutations at the protein level. The tool excels by identifying complex protein variants arising from alternative splicing, gene fusions, and other intricate genetic modifications often missed by traditional methods. moPepGen's graph-based framework enables a more comprehensive proteogenomic analysis, offering promise for cancer immunotherapy, personalized vaccines, and studies of neurodegenerative diseases. The advance addresses a critical bottleneck in linking genetic alterations to functional protein changes, as detailed in a Nature Biotechnology publication.