Metabolomics has been referred to as the apogee of the omics-sciences,  as it is closest to the biological phenotype. Metabolites are not only responsible for tasks such as growth, development, and reproduction, but also directly relevant to structure, signaling, and chemical 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, and the data processing and metabolite identification form the largest bottlenecks in high-throughput  analysis. Unlike proteomics, where close cooperation 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 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 breakout 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 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, CFMID and others be improved?
  
• Identification statistics: What statistics 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 publicly available reference data?

Classification :

• Bioinformatics

Keywords :

• Bioinformatics
• Cheminformatics
• Computational metabolomics
• Computational mass spectrometry
• Algorithms
• Databases