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Computational Proteomics (Dagstuhl Seminar 23301)

Authors: Rebekah Gundry, Lennart Martens, and Magnus Palmblad

Published in: Dagstuhl Reports, Volume 13, Issue 7 (2024)

Abstract

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.

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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)

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@Article{gundry_et_al:DagRep.13.7.152,
  author =	{Gundry, Rebekah and Martens, Lennart and Palmblad, Magnus},
  title =	{{Computational Proteomics (Dagstuhl Seminar 23301)}},
  pages =	{152--165},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{7},
  editor =	{Gundry, Rebekah and Martens, Lennart and Palmblad, Magnus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.13.7.152},
  URN =		{urn:nbn:de:0030-drops-197788},
  doi =		{10.4230/DagRep.13.7.152},
  annote =	{Keywords: bioinformatics, glycoproteomics, machine learning, mass spectrometry, proteomics, single cell proteomics}
}

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DOI: 10.4230/DagRep.11.6.1

Computational Proteomics (Dagstuhl Seminar 21271)

Authors: Sebastian Böcker, Rebekah Gundry, Lennart Martens, and Magnus Palmblad

Published in: Dagstuhl Reports, Volume 11, Issue 6 (2021)

Abstract

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.

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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)

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@Article{bocker_et_al:DagRep.11.6.1,
  author =	{B\"{o}cker, Sebastian and Gundry, Rebekah and Martens, Lennart and Palmblad, Magnus},
  title =	{{Computational Proteomics (Dagstuhl Seminar 21271)}},
  pages =	{1--13},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2021},
  volume =	{11},
  number =	{6},
  editor =	{B\"{o}cker, Sebastian and Gundry, Rebekah and Martens, Lennart and Palmblad, Magnus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.6.1},
  URN =		{urn:nbn:de:0030-drops-155775},
  doi =		{10.4230/DagRep.11.6.1},
  annote =	{Keywords: bioinformatics, computational mass spectrometry, machine learning, proteomics}
}

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Computational Proteomics (Dagstuhl Seminar 23301)

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Computational Proteomics (Dagstuhl Seminar 21271)

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