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. 2015 Jul 1;43(W1):W251-7.
doi: 10.1093/nar/gkv380. Epub 2015 Apr 20.

MetaboAnalyst 3.0--making Metabolomics More Meaningful

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Free PMC article

MetaboAnalyst 3.0--making Metabolomics More Meaningful

Jianguo Xia et al. Nucleic Acids Res. .
Free PMC article

Abstract

MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms.

Figures

Figure 1.
Figure 1.
MetaboAnalyst 3.0 Flowchart. This figure illustrates the general logic and data processing pipeline behind MetaboAnalyst. Different functions will be applied to process different types of data into matrices. The red boxes with dashed boundaries indicate functions that are only triggered in certain data analysis scenarios. After data integrity checks and normalization steps have been completed, downstream statistical analyses (purple box), functional analyses (green box) or advanced analyses for translational studies (orange box) can be applied. Note that different inputs are required for integrated pathway analysis and for invoking some of the general utility functions.
Figure 2.
Figure 2.
Sample screenshots from MetaboAnalyst 3.0. (A) A PLS-DA 2D scores plot with semi-transparent confidence intervals. The corresponding interactive 3D plot is shown in its top right corner. (B) Updated heatmaps automatically adjust their image size to ensure all data points are visible. (C) An ROC curve with a 95% confidence interval (marked in purple) generated from several manually selected biomarkers. (D) A summary plot showing the relationship between different sample sizes and predicted powers.

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