PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks.

Journal: Bioinformatics (Oxford, England)
Published Date: Sep 1, 2024

Abstract

MOTIVATION: Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses.

Authors

  • Tobias Pietz
    Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany.
  • Sukrit Gupta
    School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
  • Christoph N Schlaffner
    Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany.
  • Saima Ahmed
    Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States.
  • Hanno Steen
    Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States.
  • Bernhard Y Renard
    Research Group Bioinformatics (NG4), Robert Koch Institute, 13353, Berlin, Germany.
  • Katharina Baum
    Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany.

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