Proteogenomic

Proteogenomics is an interdisciplinary field that integrates proteomics and genomics to improve genome annotation and to study protein expression. It combines large-scale mass spectrometry–based proteomics with genomic and transcriptomic data to provide direct evidence of protein products and to refine gene models. The central concept is to use peptide evidence identified by proteomics to validate, correct, or extend predicted protein-coding regions, and to discover novel coding regions not predicted by current annotations, such as alternative open reading frames, non-canonical translation, and alternative splicing events.

Workflow typically involves generating a customized protein sequence database derived from genomic sequences, transcript sequences, or six-frame translations, and then searching MS/MS spectra against this database. Peptide-spectrum matches are filtered with stringent false discovery rate control; high-confidence identifications are mapped back to the genome to update gene models. Additional data types such as RNA-Seq, ribosome profiling, and quantitative proteomics may be integrated to corroborate expression and refine annotations. Validations can include targeted proteomics.

Applications include applications in human health and disease research, such as cancer proteogenomics to identify tumor-specific proteins and mutations, as well as studies in plant, microbial, and other model organisms to improve genome annotations. Proteogenomics also aids in the discovery of small open reading frames and unannotated coding sequences and in characterizing alternative splicing and post-translational modifications.

Challenges include the large search space from expanded databases increasing the potential for false positives, reliance on high-quality reference genomes, complexity added by PTMs and isoforms, and the need for standardized computational pipelines and robust statistical controls to ensure reproducibility across platforms and tissues.