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December 3 – 8 , 2017, Dagstuhl Seminar 17491

Computational Metabolomics: Identification, Interpretation, Imaging

Organizers

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

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Documents

Dagstuhl Report, Volume 7, Issue 12 Dagstuhl Report
Aims & Scope
List of Participants
Shared Documents

Summary

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.

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.

Dagstuhl Seminar 17491 on Computational Metabolomics: Identification, Interpretation, Imaging built on the success of the first Computational Metabolomics Dagstuhl Seminar (15492) in 2015. A number of topics overlapped with the 2015 seminar, while the focus on imaging introduced new perspectives, participants and topics. In contrast to the first seminar, 17491 was a large seminar, with 45 very active participants and a large portion of young scientists. From the first hours of the seminar, effort was made to integrate these young scientists in the discussions and presentations and this paid off leading to lively discussions involving all participants. Many participants were new to Dagstuhl and the concept of Dagstuhl seminars, which led to a seminar that was a combination of being semi-structured and spontaneous. Very positive feedback was received from all during a comprehensive feedback session before lunch on Friday, including constructive ideas for a new focus for a possible new seminar in 2019.

On the scientific side, the seminar covered numerous topics which were found to be most relevant for the computational analysis of mass spectrometry data, and ranged from the "dark matter in metabolomics" to "integrating spatial and conventional metabolomics"; see the full report for a comprehensive description.

The seminar has fully achieved its key goals: to foster the exchange of ideas between the experimental and computational communities; to expose the novel computational developments and challenges; and, to establish collaborations to address grand and priority challenges by bridging the best available data with the best methods.

License
  Creative Commons BY 3.0 Unported license
  Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski

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Seminar Homepage : Last Update 21.10.2018, 03:11 o'clock