Computational Proteomics (Dagstuhl Seminar 23301)

Authors Rebekah Gundry, Lennart Martens, Magnus Palmblad and all authors of the abstracts in this report

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Author Details

Rebekah Gundry
  • University of Nebraska - Omaha, US
Lennart Martens
  • Ghent University, BE
Magnus Palmblad
  • Leiden University Medical Center, NL
and all authors of the abstracts in this report

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Rebekah Gundry, Lennart Martens, and Magnus Palmblad. Computational Proteomics (Dagstuhl Seminar 23301). In Dagstuhl Reports, Volume 13, Issue 7, pp. 152-165, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


This report documents the program and the outcomes of Dagstuhl Seminar 23301 "Computational Proteomics". This seminar was built around three topics: the increasingly widespread use, and continously increasing promise of advanced machine learning approaches in proteomics; the highly exciting, yet fiendishly complicated, field of single cell proteomics, and the development of novel computational methods to analyse the highly challenging data obtained from the glycoproteome. These three topics fuelled three parallel breakout sessions, which ran in parallel at any given time throughout the seminar. A fourth, cross-cutting breakout session was created during the seminar as well, which dealt with the standardisation efforts in proteomics data, and explored the possibilities to upgrade these standards to better cope with the increasing demands being put on the relevant data storage and dissemination formats. This report comprises an Executive Summary of the Dagstuhl Seminar, which describes the overall seminar structure together with the key take-away messages for each of the three topics. This is followed by the abstracts, comprising three introduction talks, one for each topic, which were intended to whet the participants' appetite for each topic, while also introducing an expert perspective on the current challenges and opportunities in that topic. Along with the topic talks, two ad-hoc talks were presented during the seminar as well, and their abstracts are provided next. Moreover, each breakout session also comes with its own abstract, which provides insights into its discussions and relevant outcomes throughout the seminar.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • bioinformatics
  • glycoproteomics
  • machine learning
  • mass spectrometry
  • proteomics
  • single cell proteomics


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