GAP-MS: Automated validation of gene predictions using integrated mass spectrometry evidence

Abstract

1.Accurate genome annotation is fundamental to modern biology, yet distinguishing authentic protein-coding sequences from prediction artifacts remains challenging, particularly in complex plant genomes where automated methods are error-prone and manual curation is rarely feasible due to prohibitive time and costs. Here, we present GAP-MS (Gene model Assessment using Peptides from Mass Spectrometry), an automated proteogenomic pipeline that leverages mass spectrometry evidence to validate the protein-level accuracy of predicted gene models. Applied across 9 major crop species, GAP-MS consistently improved the prediction precision for four widely used gene prediction tools. In addition to filtering likely erroneous models, the pipeline identified hundreds of candidate protein-coding loci absent from current standard reference annotations. These peptide-supported loci were further verified by transcriptional evidence, well-supported functional annotations, and high coding-potential scores. Together, these results demonstrate that direct proteomic evidence can help resolve annotation ambiguities, define high-confidence peptide-supported reference proteomes, and uncover overlooked protein-coding genes, while facilitating the identification of sequences that may require further investigation.