New computational algorithms are enhancing the detection of hidden protein variants resulting from complex genetic changes. UCLA and University of Toronto researchers developed moPepGen, a proteogenomics tool that identifies non-canonical peptides arising from alternative splicing, RNA editing, and gene fusions. Published in Nature Biotechnology, moPepGen improves capabilities for neoantigen discovery and personalized oncology. The technology bridges genomic data with proteomic expression, advancing diagnostics and immunotherapy development for cancer and other diseases.