Computational Proteomics (Dagstuhl Seminar 21271)

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

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Author Details

Sebastian Böcker
  • Universität Jena, DE
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

Cite AsGet BibTex

Sebastian Böcker, Rebekah Gundry, Lennart Martens, and Magnus Palmblad. Computational Proteomics (Dagstuhl Seminar 21271). In Dagstuhl Reports, Volume 11, Issue 6, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


This report documents the program and the outcomes of Dagstuhl Seminar 21271 "Computational Proteomics". The Seminar, which took place in a hybrid fashion with both local as well as online participation due to the COVID pandemic, was built around three topics: the rapid uptake of advanced machine learning in proteomics; computational challenges across the various rapidlly evolving approaches for structural and top-down proteomics; and the computational analysis of glycoproteomics data. These three topics were the focus of three corresponding breakout sessions, which ran in parallel throughout the seminar. A fourth breakout session was created during the seminar, on the specific topic of creating a Kaggle competition based on proteomics data. The abstracts presented here first describe the three introduction talks, one for each topic. These talk abstracts are then followed by one abstract each per breakout session, documenting that breakout’s discussion and outcomes. An Executive Summary is also provided, which details the overall seminar structure alongside the most important conclusions for the three topic-derived breakouts.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • bioinformatics
  • computational mass spectrometry
  • machine learning
  • proteomics


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