December 3 – 8 , 2017, Dagstuhl Seminar 17491

Computational Metabolomics: Identification, Interpretation, Imaging


Theodore Alexandrov (EMBL Heidelberg, DE)
Sebastian Böcker (Universität Jena, DE)
Pieter Dorrestein (UC – San Diego, US)
Emma Schymanski (University of Luxembourg, LU)

For support, please contact

Annette Beyer for administrative matters

Michael Gerke for scientific matters


List of Participants
Shared Documents


Metabolomics is the study of metabolites (the small molecules involved in metabolism) in living cells, cell populations, organisms or communities. Metabolites are key players in almost all biological processes, play various functional roles providing energy, building blocks, signaling, communication, and defense and serve as clinical biomarkers for detecting medical conditions such as cancer. Small molecule drugs (many of which are derived from metabolites) account for 90 % of prescribed therapeutics. Complete understanding of biological systems requires detecting and interpreting the metabolome in time and space.

Mass spectrometry is the predominant analytical technique for detecting and identifying metabolites and other small molecules in high–throughput experiments. Huge technological advances in mass spectrometry and experimental workflows during the last decade enabled novel investigations of biological systems on the metabolite level. Research into computational workflows, the simulation of tandem mass spectra, compound identification and molecular networking have helped disentangle the vast amount of information that mass spectrometry provides. Spatial metabolomics on different spatial scales from single cells to organs and organisms has posed data analysis challenges, in particular due to an unprecedented data volume generated that grows quadratically with the increase of spatial resolution.

Other omics fields have benefited greatly from close cooperations between experimental and computational scientists, which is still in its infancy for metabolomics. Many of the methods established in other omics fields, especially proteomics, are not directly transferable to metabolomics due to the striking chemical variety of metabolites. Here, lessons learned in other fields such as pharmaceutical and environmental sciences, forensics, and toxicology are invaluable to extending the window of metabolomics beyond its current state. This seminar aims to foster communication between the computational and experimental scientists to bring metabolomics in line with the other omics fields.

Continued improvements to instruments, resolution, ionization and acquisition techniques mean that metabolomics mass spectrometry experiments can generate massive amounts of data, and the field is evolving into a “big data” science. This is particularly the case for imaging mass spectrometry, where a single dataset can easily be many gigabytes or even terabytes in size. Despite this dramatic increase in data, much of the data analysis in metabolomics is still performed manually and requires expert knowledge as well as the collation of data from a plethora of sources. Novel computational methods are required to exploit spectral and, in the case of imaging, also spatial information from the data, while remaining efficient enough to process tens to hundreds of gigabytes of data.

This Dagstuhl Seminar will build upon the success of the first Computational Metabolomics Dagstuhl Seminar (15492). It will address the core themes and challenges applicable to computational metabolomics, while adding a parallel focus on spatial aspects and imaging mass spectrometry. The key goals are (i) to foster the exchange of ideas between the experimental and computational communities, (ii) to expose the novel computational developments and challenges, and (iii) establish collaborations to address grand and priority challenges by bridging the best available data with the best methods.

The seminar will start with leaders in the field presenting current opportunities and computational challenges in classical metabolomics, imaging and environmental sciences. Main topics will include MS1 and MS/MS analysis, feature building, annotation and un-annotated “dark matter”, structural identification, substructure recognition, computational workflows and services, as well as network-based or integrative analysis. Several breakout sessions throughout the seminar will facilitate discussion of multiple smaller topics and cater to all participants. Potential topic leaders and speakers will be contacted in advance. We look forward to lively discussions and contributions from all participants.

  Creative Commons BY 3.0 DE
  Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, Emma Schymanski, and Nicola Zamboni

Related Dagstuhl Seminar


  • Bioinformatics


  • Computational metabolomics
  • Computational mass spectrometry
  • Imaging mass spectrometry
  • Bioinformatics
  • Chemoinformatics

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