AI Article Synopsis
- Analyzing omics data, particularly metabolomics, faces challenges in linking individual metabolite changes to biological processes, especially due to the prevalence of unknown metabolites in untargeted LC-MS studies.
- Tools like CorrelationCalculator and Filigree were developed to improve data network analysis by enabling the construction of correlation-based networks even when the number of metabolites exceeds the sample size.
- These tools enhance the understanding of metabolomics data through features like single network construction and differential network analysis, making them valuable for real-life applications in biological research.
Article Abstract
A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.
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| http://dx.doi.org/10.3791/65512 | DOI Listing |
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