Next level of reproducible, comparable and integrable Metabolomics: An ELIXIR Implementation Study

Le résumé fourni par la source

The ELIXIR Metabolomics Community relies on the development, adoption and continuous use of standards, formats and data processing and analysis solutions. Challenges persist in ensuring high-quality reported metadata, sufficiently contextualised results and interoperable and reusable datasets that hinder integrating metabolomics data with other omics or other studies. We introduce the ELIXIR Implementation Study to progress towards the Next level of Reproducible, Comparable and Integrable Metabolomics (RCI-M). RCI-M has been designed to address these issues by connecting key international standards with ELIXIR resources , and creating associated community guidelines and training materials. Based on the ELIXIR FAIRification framework , the RCI-M project will: 1) increase interoperability and re-use of public metabolomics datasets and workflows through enhanced and extended open data standards, resources and new semantic annotations; 2) define, ensure and establish quality control for study baselines in Metabolomics and Exposomics; and 3) facilitate metabolomic data interpretation and meta-analysis integration with multi-omics and systems biology studies. As a first necessary step, RCI-M will create a Semantic Metabolomics Data Model to standardise metadata, ensuring unambiguous re-use of metabolomics studies and results. The model will focus on integrating key ontologies, providing open training initiatives, and enhancing the interoperability and reproducibility of processing and analysis of metabolomics data through the production of open guidelines for annotation steps. By linking with ELIXIR’s Deposition Databases , the ISA Framework , and other services, RCI-M seeks to boost interconnection with ELIXIR Platforms , other ELIXIR Communities (e.g., System Biology , Food & Nutrition , Galaxy , Proteomics , Toxicology , RDA and Single-Cell Omics ), and the FAIR Cookbook and Bioschemas.org communities. We thus facilitate new ambitious and innovative solutions using semantically enriched data for inter-comparison of studies in the healthcare, life science, clinical and plant domains, and lay the foundation for AI-enabled metabolomics.

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