Proteogenomics is a field of biological research that utilizes a combination of proteomics, genomics, and transcriptomics to aid in the discovery and identification of peptides. Proteogenomics is used to identify new peptides by comparing MS/MS spectra against a protein database that has been derived from genomic and transcriptomic information. Proteogenomics often refers to studies that use proteomic information, often derived from mass spectrometry, to improve gene annotations. The utilization of both proteomics and genomics data alongside advances in the availability and power of spectrographic and chromatographic technology led to the emergence of proteogenomics as its own field in 2004.
Proteomics deals with proteins in the same way that Genomics studies the genetic code of entire organisms, while Transcriptomics deals with the study of RNA sequencing and transcripts. While all three fields might use forms of mass spectrometry and chromatography to identify and study the functions of DNA, RNA, and proteins, proteomics relies on the assumption that current gene models are correct and that all relevant protein sequences can be found in a reference database such as the Proteomics Identifications Database. Proteogenomics helps eliminate this reliance on existing, limited genetic models by combining datasets from multiple fields in order to produce a database of proteins or genetic markers. In addition, the emergence of novel protein sequences due to mutations often cannot be accounted for in traditional proteomic databases, but can be predicted and studied using a synthesis of genomic and transcriptomic data.
The resulting research has applications in improving gene annotations, studying mutations, and understanding the effects of genetic manipulation.
More recently, the joint profiling of surface proteins and mRNA transcripts from single cells by methods such as CITE-Seq and ESCAPE has been referred to as single-cell proteogenomics, although the goals of these studies are not related to peptide identification. Since 2019 these methods are more commonly referred to as multimodal omics or multi-omics.
History
Proteogenomics emerged as an independent field in 2004, based on the integration of technological advancements in next-generation sequencing genomics, and mass spectrometry proteomics. The term itself came into use that year, with the publication of a paper by George Church’s research group describing their discovery of a proteogenomic mapping technique that utilized proteomics data to better annotate the genome of the bacteria M. pneumoniae. By using a modern protein database, the lab mapped peptides detected in a whole cell onto a genetic scaffold using tandem mass spectrometry, then used the generated "hits" in order to create a "proteogenomic map" based on traditional genetic signals. The resulting map proved extremely accurate, with over 81% of predicted genomic reading frames being detected in the bacterial cells studied. In addition, the lab discovered several new frames not predicted via purely genetic methods, as well as some evidence supporting the idea that several predictions based genetic models could be false, proving the accuracy and cost-effectiveness of the hybrid technique.
The field expanded over the next two decades, initially using proteomics data to aid in refining genetic models via protein databases. In 2020s, one of the most common technique for identifying peptides involves using tandem mass spectrometry. This technique originated with Eng and Yates in 1994 which involves comparing a theoretical peptide fragment spectrum to compare an experimentally derived peptide spectrum to and outputting the most likely matches found. However, in the absence of an established peptide database, Proteogenomics instead compares the experimental spectrum to a genomic database instead which can then be used for genome annotation - as described in George Church's work.[3] The latter technique has become more widely used over the last decade in large part due to the increasing affordability and speed of genomic sequencing techniques coupled with the increasing sensitivity of mass spectrometry-based proteomics.
Methodology
The main idea behind the proteogenomic approach is to identify peptides by comparing MS/MS data to protein databases that contain predicted protein sequences. The protein database is generated in a variety of ways through the utilization of genomic and transcriptomic data. Below are some of the ways in which protein databases are generated: