Proteogenomics: concepts, applications and computational strategies

A proteogenomic approach to analyzing mass spectrometry–based proteomic data enables the discovery of novel peptides, provides peptide-level evidence of gene expression, and assists in refining gene models. Strategies for building custom sequence databases, applications benefitting from a proteogenomic approach, and challenges in interpreting data are discussed in this Review. Also in this issue, Alfaro et al. discuss the use of proteogenomic approaches for studying cancer biology. Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry–based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry–based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.