MetaboAnalyst 3.0—making metabolomics more meaningful

TLDR

MetaboAnalyst is a web server that enables comprehensive metabolomic data analysis, visualization, and interpretation, supporting complex statistical calculations and high‑quality graphics, and has grown over 50‑fold since its 2009 launch to process more than 50,000 jobs monthly. The authors rewrote and upgraded the server to meet increasing computational demands and support translational and systems biology applications. The new version, MetaboAnalyst 3.0, adds biomarker analysis via ROC curves, sample‑size estimation and power analysis, and integrative pathway analysis for genes and metabolites, while enhancing graphics, libraries, and organism coverage. These changes substantially improve performance, capacity, and user interactivity, resulting in a more powerful platform.

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.