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Computational Metabolomics: From Spectra to Knowledge (Dagstuhl Seminar 22181)

Authors: Corey Broeckling, Timothy Ebbels, Ewy Mathé, Nicola Zamboni, and Cecilia Wieder

Published in: Dagstuhl Reports, Volume 12, Issue 5 (2022)

Abstract

The fourth edition of the Computational Metabolomics seminars, Dagstuhl Seminar 22181, brought together a wide range of computational and experimental experts to share state-of-the art methodologies and push our collective understanding of how to interpret and maximise insight of metabolomic data. With increasing amounts of metabolomic data being generated, including large-scale epidemiological studies, and increasing sensitivity of instrumentation, development of sophisticated and robust computational solutions is required. Further, community agreement on which data standards should be used and which data sets are most apt for benchmarking computational tools is needed in the field. Building upon the previous successful formats of previous seminars (17491, 15492, and 20051) on this topic, attendees gathered each morning to collectively agree on the number of sessions and topics to discuss. A summary of the daily sessions were shared amongst all participants after dinner during each day’s final formal session. Further, informal evening sessions were spontaneously created to further dive into specific topics. As with past seminars, this format was very well received and enabled all participants to weigh in. Of particular note, this seminar was delayed and travel was complicated due to the pandemic. Despite these setbacks, this seminar brought together a balanced number of previous and new, seasoned and early career participants. All participants were active in these discussions, and a true sense of renewed energy ensued from the seminar. This report provides highlights of formal and informal evening sessions, including future anticipated research directions rooted from this seminar. Possible future workshops, such as a next phase of this Computational Metabolomics Dagstuhl seminar in late 2023 or 2024 were also discussed and will be applied for.

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Corey Broeckling, Timothy Ebbels, Ewy Mathé, Nicola Zamboni, and Cecilia Wieder. Computational Metabolomics: From Spectra to Knowledge (Dagstuhl Seminar 22181). In Dagstuhl Reports, Volume 12, Issue 5, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{broeckling_et_al:DagRep.12.5.1,
  author =	{Broeckling, Corey and Ebbels, Timothy and Math\'{e}, Ewy and Zamboni, Nicola and Wieder, Cecilia},
  title =	{{Computational Metabolomics: From Spectra to Knowledge (Dagstuhl Seminar 22181)}},
  pages =	{1--16},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{12},
  number =	{5},
  editor =	{Broeckling, Corey and Ebbels, Timothy and Math\'{e}, Ewy and Zamboni, Nicola and Wieder, Cecilia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.5.1},
  URN =		{urn:nbn:de:0030-drops-174410},
  doi =		{10.4230/DagRep.12.5.1},
  annote =	{Keywords: bioinformatics, cheminformatics, computational mass spectrometry, metabolite identification, computational metabolomics, machine learning, data integration, pathway analysis}
}

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DOI: 10.4230/DagRep.10.1.144

Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)

Authors: Sebastian Böcker, Corey Broeckling, Emma Schymanski, and Nicola Zamboni

Published in: Dagstuhl Reports, Volume 10, Issue 1 (2020)

Abstract

Dagstuhl Seminar 20051 on Computational Metabolomics is the third edition of seminars on this topic and focused on Cheminformatics and Machine Learning. With the advent of higher precision instrumentation, application of metabolomics to a wider variety of small molecules, and ever increasing amounts of raw and processed data available, developments in cheminformatics and machine learning are sorely needed to facilitate interoperability and leverage further insights from these data. Following on from Seminars 17491 and 15492, this edition convened both experimental and computational experts, many of whom had attended the previous sessions and brought much-valued perspective to the week’s proceedings and discussions. Throughout the week, participants first debated on what topics to discuss in detail, before dispersing into smaller, focused working groups for more in-depth discussions. This dynamic format was found to be most productive and ensured active engagement amongst the participants. The abstracts in this report reflect these working group discussions, in addition to summarising several informal evening sessions. Action points to follow-up on after the seminar were also discussed, including future workshops and possibly another Dagstuhl seminar in late 2021 or 2022.

Cite as

Sebastian Böcker, Corey Broeckling, Emma Schymanski, and Nicola Zamboni. Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051). In Dagstuhl Reports, Volume 10, Issue 1, pp. 144-159, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{bocker_et_al:DagRep.10.1.144,
  author =	{B\"{o}cker, Sebastian and Broeckling, Corey and Schymanski, Emma and Zamboni, Nicola},
  title =	{{Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)}},
  pages =	{144--159},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2020},
  volume =	{10},
  number =	{1},
  editor =	{B\"{o}cker, Sebastian and Broeckling, Corey and Schymanski, Emma and Zamboni, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.10.1.144},
  URN =		{urn:nbn:de:0030-drops-124036},
  doi =		{10.4230/DagRep.10.1.144},
  annote =	{Keywords: bioinformatics, chemoinformatics, computational mass spectrometry, computational metabolomics, machine learning}
}

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Computational Metabolomics: From Spectra to Knowledge (Dagstuhl Seminar 22181)

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Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)

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