A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Abstract

Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies.

Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform

“…Quantitative changes in the proteome can also be correlated with transcript abundance changes [ 39 ] to get a more complete picture of how an organism responds to a stimulus. For example, in cancer proteogenomics studies, these abundance measurements help identify differential expression patterns of variant peptides that may have functional significance in cancer [ 40 , 41 ].…”

Precursor intensity-based label-free quantification software tools for proteomic and multiomic analysis within the Galaxy Platform

“…Quantitative changes in the proteome can also be correlated with transcript abundance changes [36] to get a more complete picture of how an organism responds to a stimulus. For example, in cancer proteogenomics studies, these abundance measurements help identify differential expression patterns of variant peptides that may have functional significance in cancer [37,38].…”