“A community-based protocol for the statistical analysis of non-targeted metabolomics data”

Non-targeted metabolomics is distinguished from its targeted counterpart by its exploratory nature, which aims to capture the entire spectrum of small molecules present in a sample. This approach typically generates large, complex datasets that require sophisticated analysis tools to identify and interpret the relevant chemical signatures that reflect underlying biological processes. Several tools and platforms have been developed to aid in this process, notably Feature-based Molecular Networking (FBMN) within the Global Natural Products Social Molecular Networking (GNPS) metabolomics cloud ecosystem. FBMN has become a cornerstone in metabolomics research, enabling researchers to annotate and connect features across samples. However, the subsequent statistical analysis of these features has remained a significant roadblock, particularly for those who are not experts in computational methods. The fragmented nature of available tools, scattered across different platforms and requiring customized scripts, adds to the challenge, especially for newcomers to the field. The need for a comprehensive, user-friendly guide that integrates multiple statistical approaches into a cohesive analysis pipeline became increasingly apparent.

To address these challenges, Daniel Petras and his team developed a detailed protocol that guides researchers through the entire process of analyzing FBMN results. This protocol, designed to be an end-to-end solution, begins with feature detection and continues through data clean-up, statistical analysis, and spectrum annotation. By providing ready-made code for the popular statistical platforms R and Python, as well as a graphical user interface (GUI), they aimed to make the tool kit accessible to a wide range of users. The protocol is fully integrated with FBMN, and the input files can be directly loaded from GNPS , ensuring seamless workflow compatibility. For users who prefer a more interactive approach, they developed a web application with a GUI, available both online and as a downloadable application. This tool is designed not only for experienced researchers but also for educational purposes, making it an ideal resource for students and early-career scientists.

This protocol was developed with the support of the Virtual Multiomics Lab (VMOL), a community-driven, open-access virtual laboratory. Initiated in 2022, this project aims to democratize access to non-targeted metabolomics analysis strategies, workflows, and expertise, making computational mass spectrometry accessible to researchers worldwide, regardless of their background or resources.

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