January 26 – 31 , 2020, Dagstuhl Seminar 20051

Computational Metabolomics: From Cheminformatics to Machine Learning


Sebastian Böcker (Universität Jena, DE)
Corey Broeckling (Colorado State University – Fort Collins, US)
Emma Schymanski (University of Luxembourg, LU)
Nicola Zamboni (ETH Zürich, CH)

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Dagstuhl Report, Volume 10, Issue 1 Dagstuhl Report
Aims & Scope
List of Participants
Dagstuhl's Impact: Documents available


Mass spectrometry is the predominant analytical technique for detection, identification, and quantification in metabolomics experiments. Technological advances in mass spectrometry and experimental workflows during the last decade enabled novel investigations of biological systems on the metabolite level. Metabolomics started as the study of all metabolites in a living cell or organism; in comparison to transcriptome and proteome, the metabolome is a better proxy of metabolic activity. Emerging fields including personalized medicine and exposomics have expanded the scope of metabolomics to “all” small molecules, including those of non-biological origin. Advances in instrumentation plus rapid increase in popularity, throughput and desired compound coverage has resulted in vast amounts of both raw and processed data; the field is in desperate need for further developments in computational methods. Methods established in other -omics fields are frequently not transferable to metabolomics due to the structural diversity of small molecules. This third Dagstuhl Seminar on Computational Metabolomics (following Seminars 15492 and 17491) focused on cheminformatics and machine learning. The seminar was less structured than previous seminars, forming break-out sessions already from Monday afternoon, then collecting participants back into plenary sessions at regular intervals for discussions and further topic exploration. The major topics launched on Monday included cheminformatics, genome mining and autoencoders, which were developed throughout the day. Other topics discussed throughout the week included biosynthesis and gene clusters, confidence and compound identification, spectral versus structural similarity, statistical integration, collision cross section (CCS) and ion mobility separation (IMS), benchmarking data, open feature file format, exposomics, data processing and acquisition. Several evening sessions were also held, including retention time, Bioschemas, MassBank, ethics and philosophy of software development, open biological pathways, mass spec health check, Jupyter notebooks, a mini decoy session and a session on coding tips. The excursion, breaking with previous Christmas Market traditions, was to the Völklingen steelworks. Finally, the entire seminar was wrapped up with a discussion on the future of untargeted metabolomics on Friday – time will tell what the future Computational Metabolomics Seminars will bring. A further seminar in the series may be considered for the end of 2021 or in 2022.

Summary text license
  Creative Commons BY 3.0 Unported license
  Sebastian Böcker, Corey Broeckling, Emma Schymanski, and Nicola Zamboni

Dagstuhl Seminar Series


  • Artificial Intelligence / Robotics
  • Bioinformatics


  • Computational metabolomics
  • Computational mass spectrometry
  • Bioinformatics
  • Chemoinformatics
  • Machine learning


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