Fast, Quantitative and Variant Enabled Mapping of Peptides to Genomes.

Christoph Schlaffner, Georg Pirklbauer, Andreas Bender, Jyoti Choudhary

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


Current tools for visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies and capture only basic sequence identity information. Furthermore, the frequent reformatting of annotations for reference genomes required by these tools is known to be highly error prone. We developed PoGo for mapping peptides identified through mass spectrometry to overcome these limitations. PoGo reduced runtime and memory usage by 85% and 20%, respectively, and exhibited overall superior performance over other tools on benchmarking with large-scale human tissue and cancer phosphoproteome datasets comprising ∼3 million peptides. In addition, extended functionality enables representation of single-nucleotide variants, post-translational modifications, and quantitative features. PoGo has been integrated in established frameworks such as the PRIDE tool suite and OpenMS, as well as a standalone tool with user-friendly graphical interface. With the rapid increase of quantitative high-resolution datasets capturing proteomes and global modifications to complement orthogonal genomics platforms, PoGo provides a central utility enabling large-scale visualization and interpretation of transomics datasets.
Original languageEnglish
Pages (from-to)152-156.e4
JournalCell Systems
Issue number2
Publication statusPublished - 23 Aug 2017


  • annotation
  • genome browser
  • genomics
  • large-scale
  • mapping
  • open-source software
  • proteogenomics
  • proteomics
  • track hubs
  • visualization
  • Humans
  • Molecular Sequence Annotation
  • Peptide Mapping/methods
  • Genomics/methods
  • Cell Line, Tumor
  • Proteomics/methods
  • Software

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