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Updated: May 31, 2026
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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)
Published on: May 20, 2013
MetATT: a web-based metabolomics tool for analyzing time-series and two-factor datasets.
Jianguo Xia1, Igor V Sinelnikov, David S Wishart
1Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada. jianguox@ualberta.ca
Bioinformatics (Oxford, England)
|June 30, 2011
Summary
MetATT is a new web tool for analyzing complex metabolomic data from time-series and multifactor experiments. It helps identify variations, compare temporal profiles, and detect interactions for better biological insights.
Area of Science:
- Metabolomics
- Bioinformatics
- Systems Biology
Background:
- Time-series and multifactor studies are increasingly prevalent in metabolomics.
- Analyzing complex metabolomic data requires specialized tools for identifying variations and interactions.
Purpose of the Study:
- Introduce MetATT, a web-based tool for analyzing time-series and two-factor metabolomic data.
- Provide an intuitive platform for common metabolomic data analysis tasks.
Main Methods:
- MetATT integrates 3D interactive principal component analysis (PCA).
- Includes two-way heatmap visualization, two-way ANOVA, and ANOVA-simultaneous component analysis (ASCA).
- Features multivariate empirical Bayes time-series analysis for temporal data.
Main Results:
- The tool facilitates identification of major variations linked to experimental factors.
- Enables comparison of temporal profiles across different biological conditions.
- Supports detection and validation of interactions within the data.
Conclusions:
- MetATT offers a comprehensive suite of analytical approaches for complex metabolomic data.
- The web interface and detailed reports enhance usability and understanding of results.
- A valuable resource for researchers in metabolomics and related fields.
