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Novel algorithms have become driving forces of innovation in mass spectrometry (MS) based proteomics, as these computational advances have allowed much improved recovery of actionable information from acquired data, in turn propelling the field forward towards ever more sophisticated experimental approaches. Obviously, this also creates a highly fertile ground for interdisciplinary discussions and brainstorming on the evolution and future of computational proteomics.
We have therefore identified four highly interesting computational challenges (and thus opportunities) that have come to the foreground in the field of proteomics: (i) the quickly emerging and expanding application of Machine Learning throughout the field of proteomics; (ii) dedicated 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) computational approaches to support the increasingly important role of proteomics in the discovery, design, and quality control of (novel) therapeutics.
Machine Learning is becoming pervasive across the field, which also means it cuts across the other topics, while the other three challenges represent specific application domains of proteomics (single-cell analysis, glycoprotein characterization, and therapeutics). However, between these application domains there are very interesting areas of overlap, such as analysis of therapeutic antibody glycosylation, and glycan analysis of the surfaces of single cells. This Dagstuhl Seminar on Computational Proteomics is therefore built around these four core challenges/opportunities.
As different experts need to be brought together to tackle these four topics, this seminar is thoroughly interdisciplinary: computer scientists, bioinformaticians and statisticians, who develop algorithms and software for data interpretation; experimental life scientists that rely on proteomics as a key means to elucidate biology; and analytical chemists and engineers that develop new instruments and approaches to deliver ever more comprehensive and accurate data. Throughout, industry plays crucial roles as instrument and software vendors, and as advanced users driving applications, including in the development of new diagnostics and pharmaceuticals. Industry participation is therefore explicitly included in this seminar.
Invitees are therefore coming from a very diverse group of participants, including computational and experimental scientists, and academic and industrial researchers across the four relevant domains of this seminar. The goal is to uncover as-yet unexplored synergies from interactions within and across these various backgrounds; a process which has already proven highly effective and inspiring in previous Dagstuhl Seminars on Computational Proteomics. A strong focus throughout the week thus will be on the free exchange of ideas between participants of different backgrounds to maximally benefit from obvious as well as less obvious synergies and to provide maximal opportunity for cross-fertilization of ideas. To accommodate this, the seminar structure will be quite flexible, allowing for spontaneous working groups to emerge alongside pre-planned ones, and providing opportunity for any interested participant to start a discussion on any related topic of interest.
This interdisciplinary Dagstuhl Seminar is therefore poised to enable novel, breakthrough developments in computational proteomics around the four topics: (i) new applications and methods for advanced machine learning in computational proteomics; (ii) address computational challenges posed by single-cell proteomics; (iii) build on a combination of cutting edge algorithms and novel computational approaches to allow glycan analysis in glycoproteomics; and (iv) reinforce the computational foundation of fast-growing proteomics applications in discovery, characterization, and quality control of (novel) therapeutics.
- Kiyoko Aoki-Kinoshita (Soka University - Tokyo, JP) [dblp]
- Robbin Bouwmeester (Ghent University, BE) [dblp]
- Robert Chalkley (University of California - San Francisco, US) [dblp]
- Bernard Delanghe (Thermo Fisher GmbH - Bremen, DE)
- Viktoria Dorfer (University of Applied Sciences Upper Austria, AT) [dblp]
- Melanie Föll (Universitätsklinikum Freiburg, DE) [dblp]
- Laurent Gatto (University of Louvain, BE) [dblp]
- Arzu Tugce Guler (Leiden, NL) [dblp]
- Rebekah Gundry (University of Nebraska - Omaha, US) [dblp]
- Tiannan Guo (Westlake University - Hangzhou, CN) [dblp]
- Catherine Hayes (Swiss Institute of Bioinformatics - Geneva, CH) [dblp]
- Michael Hoopmann (Institute for Systems Biology - Seattle, US) [dblp]
- Lukas Käll (KTH Royal Institute of Technology - Solna, SE) [dblp]
- Ville Koskinen (Matrix Science Ltd. - London, GB)
- Lennart Martens (Ghent University, BE) [dblp]
- Karina Martinez (George Washington University - Washington, DC, US)
- Sriram Neelamegham (University at Buffalo - SUNY, US) [dblp]
- Magnus Palmblad (Leiden University Medical Center, NL) [dblp]
- Erdmann Rapp (MPI - Magdeburg, DE)
- Tobias Schmidt (MSAID - Garching, DE) [dblp]
- Veit Schwämmle (University of Southern Denmark - Odense, DK) [dblp]
- Mathias Wilhelm (TU München - Freising, DE) [dblp]
- Dirk Winkelhardt (Ruhr-Universität Bochum, DE & ELIXIR Germany - Jülich, DE)
- Bernd Wollscheid (ETH Zürich, CH) [dblp]
- Gamze Nur Yapici (Koc University - Istanbul, TR)
- Dagstuhl-Seminar 05471: Computational Proteomics (2005-11-20 - 2005-11-25) (Details)
- Dagstuhl-Seminar 08101: Computational Proteomics (2008-03-02 - 2008-03-07) (Details)
- Dagstuhl-Seminar 13491: Computational Mass Spectrometry (2013-12-01 - 2013-12-06) (Details)
- Dagstuhl-Seminar 15351: Computational Mass Spectrometry (2015-08-23 - 2015-08-28) (Details)
- Dagstuhl-Seminar 17421: Computational Proteomics (2017-10-15 - 2017-10-20) (Details)
- Dagstuhl-Seminar 19351: Computational Proteomics (2019-08-25 - 2019-08-30) (Details)
- Dagstuhl-Seminar 21271: Computational Proteomics (2021-07-04 - 2021-07-09) (Details)
- Artificial Intelligence
- Machine Learning
- Other Computer Science
- machine learning
- mass spectrometry