Background: Metabolomics has the promise to transform the area of personalized medicine with the rapid\ndevelopment of high throughput technology for untargeted analysis of metabolites. Open access, easy to use,\nanalytic tools that are broadly accessible to the biological community need to be developed. While technology\nused in metabolomics varies, most metabolomics studies have a set of features identified. Galaxy is an open access\nplatform that enables scientists at all levels to interact with big data. Galaxy promotes reproducibility by saving\nhistories and enabling the sharing workflows among scientists.\nResults: SECIMTools (SouthEast Center for Integrated Metabolomics) is a set of Python applications that are\navailable both as standalone tools and wrapped for use in Galaxy. The suite includes a comprehensive set of quality\ncontrol metrics (retention time window evaluation and various peak evaluation tools), visualization techniques\n(hierarchical cluster heatmap, principal component analysis, modular modularity clustering), basic statistical analysis\nmethods (partial least squares - discriminant analysis, analysis of variance, t-test, Kruskal-Wallis non-parametric test),\nadvanced classification methods (random forest, support vector machines), and advanced variable selection tools\n(least absolute shrinkage and selection operator LASSO and Elastic Net).\nConclusions: SECIMTools leverages the Galaxy platform and enables integrated workflows for metabolomics data\nanalysis made from building blocks designed for easy use and interpretability. Standard data formats and a set of\nutilities allow arbitrary linkages between tools to encourage novel workflow designs. The Galaxy framework enables\nfuture data integration for metabolomics studies with other omics data.
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