Metabolomics has been referred to as the apogee of the omics-sciences, as it is closest to the biological phenotype. Metabolites are responsible for tasks such as growth, development, and reproduction, but also structure, signaling, chemical warfare, or interactions with other organisms. Metabolomics also plays an essential role in the investigation of novel drug leads or profiling metabolites of pharmaceutical compounds to detect and understand side effects. With advances in instrumentation, metabolomics is currently at the edge of becoming a "big data" science.

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 spectrometers and experimental workflows during the last decades enable novel investigations of biological systems on the metabolite level. But these advances also resulted in a tremendous increase of both amount and complexity of the experimental data, such that the data processing and identification of the detected metabolites form the largest bottlenecks in high-throughput analysis. Unlike proteomics, where close cooperations between experimental and computational scientists have been established over the last decade, such cooperation is still in its infancy for metabolomics.

The key goal of this Dagstuhl seminar is to foster the exchange of ideas between the experimental (analytical chemistry and biology) and computational (computer science and bioinformatics) communities. State-of-the-art methods from computer science, statistics, analytical and biological experiments will be presented, along with problems arising from these techniques. Brainstorming sessions and break-out groups will discuss individual topics in greater detail, to initiate new collaborations between participants who have not yet worked together. This exchange of expertise is needed to form a scientific community ready to advance computational metabolomics.

A selection of topics to initiate discussions at the seminar include:

• Searching in molecular structure databases: How can the promising approaches MetFrag, MAGMa, FingerID, LipidBlast, CFM-ID and others be improved?
• Identification statistics: What statistical can be incorporated to improve identification quality of metabolites, such as False Discovery Rates?
• Experimental frontiers: Incorporation of experimental strategies such as data-independent acquisition (DIA), ultrahigh resolution, imaging mass spectrometry, 2-dimensional chromatography etc. in metabolomics.
• Labeling: Development of novel computational methods for analyzing the data from labeling experiments to gain learn about metabolic transformations.
• Quantification and biomarker discovery: Many computational challenges remain to be discussed in these fields.
• Incorporating experimental knowledge into computational methods: How can experimentalists add their knowledge into automated procedures?
• Screening methods and metabolite prediction: Can we improve the methods to help "look" for metabolites rather than performing non-target identification?
• Data exchange and public reference data: How can metabolomics researchers be encouraged to provide additional training data that covers a sufficient breadth of the expected molecular space?
• Publication standards for computational methods: Can current standards be improved, consolidated and harmonized, to ensure consistent presentation of methods and publically-available reference data?

• Felicity Allen (University of Alberta - Edmonton, CA) [dblp]
• Nuno Bandeira (University of California - San Diego, US) [dblp]
• Sebastian Böcker (Universität Jena, DE) [dblp]
• Corey Broeckling (Colorado State University - Fort Collins, US)
• Celine Brouard (Aalto University, FI) [dblp]
• Jacques Corbeil (University Laval - Québec, CA) [dblp]
• Pieter Dorrestein (UC - San Diego, US) [dblp]
• Kai Dührkop (Universität Jena, DE) [dblp]
• P. Lee Ferguson (Duke University - Durham, US)
• Franziska Hufsky (Universität Jena, DE) [dblp]
• Gabi Kastenmüller (Helmholtz Zentrum - München, DE) [dblp]
• Tobias Kind (University of California - Davis, US) [dblp]
• Oliver Kohlbacher (Universität Tübingen, DE) [dblp]
• Daniel Krug (Helmholtz-Institut, DE)
• Kris Morreel (Ghent University, BE)
• Steffen Neumann (IPB - Halle, DE) [dblp]
• Tomas Pluskal (Whitehead Institute - Cambridge, US) [dblp]
• Lars Ridder (Netherlands eScience Center - Amsterdam, NL)
• Simon Rogers (University of Glasgow, GB) [dblp]
• Juho Rousu (Aalto University, FI) [dblp]
• Emma Schymanski (Eawag - Dübendorf, CH) [dblp]
• Huibin Shen (Aalto University, FI) [dblp]
• Christoph Steinbeck (European Bioinformatics Institute - Cambridge, GB) [dblp]
• Michael Andrej Stravs (Eawag - Dübendorf, CH)
• Ales Svatos (MPI für chemische Ökologie - Jena, DE)
• Tom Wenseleers (KU Leuven, BE) [dblp]
• Rohan Williams (National University of Singapore, SG) [dblp]
• David Wishart (University of Alberta - Edmonton, CA) [dblp]
• Michael Anton Witting (Helmholtz Zentrum - München, DE)
• Gert Wohlgemuth (University of California - Davis, US) [dblp]
• Nicola Zamboni (ETH Zürich, CH) [dblp]