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Electrophoresis. 2017 Sep;38(18):2257-2274. doi: 10.1002/elps.201700110. Epub 2017 Aug 1.

Review of emerging metabolomic tools and resources: 2015-2016.

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Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA.
Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, TX, USA.
CDS Creative Data Solutions, Ballwin, MO, USA.


Data processing and analysis are major bottlenecks in high-throughput metabolomic experiments. Recent advancements in data acquisition platforms are driving trends toward increasing data size (e.g., petabyte scale) and complexity (multiple omic platforms). Improvements in data analysis software and in silico methods are similarly required to effectively utilize these advancements and link the acquired data with biological interpretations. Herein, we provide an overview of recently developed and freely available metabolomic tools, algorithms, databases, and data analysis frameworks. This overview of popular tools for MS and NMR-based metabolomics is organized into the following sections: data processing, annotation, analysis, and visualization. The following overview of newly developed tools helps to better inform researchers to support the emergence of metabolomics as an integral tool for the study of biochemistry, systems biology, environmental analysis, health, and personalized medicine.


Data; High throughput; Mass spectrometry; Metabolomics; Software

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