Offer #34883 - FBMN

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Feature-Based Molecular Networking (FBMN): Unlocking Insights in Non-Targeted Metabolomics

Feature-Based Molecular Networking (FBMN) is a powerful analysis approach for non-targeted metabolomics data. It allows researchers to uncover valuable insights from complex LC-MS/MS datasets. In this article, we will explore the concept of FBMN and its applications in metabolomics research.

What is Feature-Based Molecular Networking?

FBMN is a data analysis technique that enables the visualization and interpretation of metabolomics data. It involves the creation of a network where each node represents a molecular feature detected in the dataset, and edges represent the similarity between features based on their mass spectra. By clustering similar features together, FBMN allows researchers to identify related compounds and discover potential metabolic pathways.

Applications of FBMN

FBMN has been widely used in various areas of metabolomics research. Some of its key applications include:

Metabolite Annotation: FBMN helps in the annotation of unknown metabolites by comparing their mass spectra with known compounds in public databases. Biomarker Discovery: FBMN can identify potential biomarkers by comparing the metabolic profiles of different sample groups. Metabolic Pathway Analysis: FBMN allows researchers to explore metabolic pathways by analyzing the connectivity between nodes in the network. Microbiome Studies: FBMN can be used to analyze the metabolic interactions between host organisms and their associated microbiota.

Statistical Analysis of FBMN Results

While processing LC-MS/MS data through FBMN is relatively straightforward, downstream statistical analysis can be challenging for new users. To address this, comprehensive guides and tools have been developed to facilitate the statistical analysis of FBMN results.

In our protocol, we provide a step-by-step guide for statistical analysis, including data clean-up, normalization, and uni- and multivariate statistical analysis. We offer code examples in R and Python, as well as guidance on using the QIIME2 framework. Additionally, we have developed a web application with a graphical user interface to simplify the process for new users.

Conclusion

Feature-Based Molecular Networking (FBMN) is a valuable tool in non-targeted metabolomics research. It allows researchers to explore complex datasets, identify unknown metabolites, and gain insights into metabolic pathways. With the availability of comprehensive guides and tools, the statistical analysis of FBMN results has become more accessible to researchers.

As FBMN continues to evolve, it holds great promise for advancing our understanding of metabolomics and its applications in various fields, including medicine, agriculture, and environmental science.

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Released on Thursday 8 August 2024 - Total : $0 - Past 30 days : $0 (1st tier : 30% - 2nd tier : 0%)

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