SECIMTools: a suite of metabolomics data analysis tools

• Published in: BMC bioinformatics 2018-04, Vol.19 (1), p.151-151, Article 151
• Main Authors: Kirpich, Alexander S, Ibarra, Miguel, Moskalenko, Oleksandr, Fear, Justin M, Gerken, Joseph, Mi, Xinlei, Ashrafi, Ali, Morse, Alison M, McIntyre, Lauren M
• Format: Article
• Language: English
• Subjects: High-throughput screening (Biochemical assaying); Metabolites; Precision medicine; Software
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2134-1

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