April 18 – 23 , 2021, Dagstuhl Seminar 21161

RESCHEDULED Computational Proteomics

Due to the Covid-19 pandemic, this seminar was rescheduled to July 23 – 28 , 2023Seminar 23301.


Rebekah Gundry (University of Nebraska – Omaha, US)
Lennart Martens (Ghent University, BE)
Magnus Palmblad (Leiden University Medical Center, NL)

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The field of proteomics continues to evolve at a rapid pace, and as it does so, it becomes more and more of a data science. Driven by methodological changes that focus increasingly on applications outside of the traditional aim to catalogue protein presence or absence, the field is in constant need of advanced informatics approaches to make sense of the acquired data. The importance of these proteomics informatics solutions is now such, that without these computational tools, the field would no longer be able to function. As a result, the field of proteomics often finds itself limited by available algorithms and computational solutions, rather than by available instruments or samples.

Based on recent developments in the field, we have identified four highly interesting computational challenges (and thus opportunities) that have come to the foreground in proteomics: (i) algorithms for the interpretation of proteomics analyses of protein structure; (ii) computational support for the newly emerging field of single-cell proteomics analyses; (iii) the identification and localization of protein glycosylation using mass spectrometry; and (iv) the quickly emerging application of Machine Learning applications throughout the field of proteomics.

While the first three challenges are clearly matched to a specific application domain of proteomics (protein structure elucidation, single-cell analysis, and glycoprotein characterization), the fourth challenge, the application of Machine Learning algorithms, presents a broadly cross-cutting development in the field, which has already started to supply innovative solutions to long-standing problems in various areas of proteomics.

The primary goal of this interdisciplinary Dagstuhl Seminar, which brings together a mix of experts from different but relevant backgrounds around these four topics, is to exchange ideas that can lead to novel algorithms and tools, and that can create novel collaborations between various participants.

A strong focus throughout the seminar will therefore be on the free exchange of ideas between the various types of expertise that will be represented among the participants, to maximally benefit from obvious as well as less obvious synergies and to provide maximal opportunity for cross-fertilization of ideas. The structure of the seminar reflects this, and consists of a mix between thought-provoking plenary lectures, and dedicated breakout sessions that have smaller groups of participants focusing on specific core questions around the four topics.

Motivation text license
  Creative Commons BY 3.0 DE
  Rebekah Gundry, Lennart Martens, and Magnus Palmblad

Dagstuhl Seminar Series


  • Machine Learning
  • Other Computer Science


  • Bioinformatics
  • Proteomics
  • Computational Mass Spectrometry
  • Machine Learning


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