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

作者： Christoph N. Schlaffner1,2,3, Georg J. Pirklbauer2, Andreas Bender3, Judith A.J. Steen1, Jyoti S. Choudhary2,4 1Department of Neurobiology, F. M. Kirby Neurobiology Center,Boston Children's Hospital, Harvard Medical School, 2Proteomic Mass Spectrometry,Wellcome Trust Sanger Institute, Wellcome Genome Campus, 3Centre for Molecular Informatics, Department of Chemistry,University of Cambridge, 4Functional Proteomics Group, Chester Beatty Laboratories,Institute of Cancer Research

简介：

Overview

This article presents PoGo, a proteogenomic tool designed for the fast and quantitative mapping of peptides identified through mass spectrometry onto reference genomes. It facilitates the integration and visualization of proteogenomic data alongside orthogonal genomics data.

Key Study Components

Area of Science

• Proteogenomics
• Mass Spectrometry
• Genomic Data Integration

Background

• PoGo enables the mapping of peptides with post-translational modifications.
• It supports integration with RNA sequencing data.
• The tool is designed for ease of use and speed.
• It accommodates additional information such as quantitation and amino acid variance.

Purpose of Study

• To demonstrate the use of PoGo for peptide mapping.
• To facilitate the integration of proteomic and genomic data.
• To provide a user-friendly interface for researchers.

Methods Used

• Installation and setup of the PoGo software.
• Mapping of peptides to reference genomes using GUI and command line.
• Visualization of peptide data in the integrative genomics viewer.
• Comparison of PoGo with other proteogenomic tools.

Main Results

• PoGo outperforms other tools in speed and memory usage.
• It effectively maps post-translational modifications and quantitative data.
• The tool provides visual aids for peptide mapping uniqueness.
• Different color codes indicate various mapping characteristics.

Conclusions

• PoGo is a valuable tool for proteogenomic studies.
• It enhances the integration of mass spectrometry data with genomic information.
• The software's efficiency and user-friendly design make it accessible for researchers.

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