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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
Published on: May 27, 2014
The future of metabolomics in ELIXIR
Merlijn van Rijswijk1,2, Charlie Beirnaert3, Christophe Caron4
1ELIXIR-NL, Dutch Techcentre for Life Sciences, Utrecht, 3503 RM, Netherlands.
European researchers are enhancing metabolomics infrastructure, focusing on metabolite identification. A new ELIXIR metabolomics Use Case is proposed to maximize computational impact and support the community.
Area of Science:
- Metabolomics
- Bioinformatics
- Computational Biology
Background:
- Metabolomics is a rapidly growing omics field with significant European research and infrastructure development.
- A strategic workshop convened ELIXIR representatives, the PhenoMeNal consortium, and international experts to focus on European metabolomics infrastructure.
Purpose of the Study:
- To coordinate and strategize infrastructure development for metabolomics in Europe.
- To identify critical areas for computational metabolomics and data management to have maximal impact.
- To propose a new ELIXIR metabolomics Use Case aligned with existing ELIXIR Platforms and Use Cases.
Main Methods:
- A one-day strategic workshop was organized involving key stakeholders in European metabolomics.
- Discussions focused on mapping community needs to ELIXIR Platforms and identifying priority Use Cases.
- The workshop identified metabolite identification as a critical area for computational advancement.
Main Results:
- Metabolite identification was established as a key area for computational metabolomics and data management.
- The workshop explored the alignment of metabolomics needs with existing ELIXIR Platforms.
- Four ELIXIR Use Cases were identified as most beneficial for the metabolomics community (industry and academia).
Conclusions:
- There is a critical need for enhanced infrastructure in metabolite identification within European metabolomics.
- A new ELIXIR metabolomics Use Case is proposed to complement existing structures and maximize impact.
- Strengthening computational metabolomics and data management is essential for advancing the field.
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