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Electrophoresis. 2016 Jan;37(1):86-110. doi: 10.1002/elps.201500417. Epub 2015 Nov 17.

Updates in metabolomics tools and resources: 2014-2015.

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Department of Biology, Genetics Institute, University of Florida, Gainesville, FL, USA.
Glasgow Polyomics, University of Glasgow, Glasgow, UK.


Data processing and interpretation represent the most challenging and time-consuming steps in high-throughput metabolomic experiments, regardless of the analytical platforms (MS or NMR spectroscopy based) used for data acquisition. Improved machinery in metabolomics generates increasingly complex datasets that create the need for more and better processing and analysis software and in silico approaches to understand the resulting data. However, a comprehensive source of information describing the utility of the most recently developed and released metabolomics resources--in the form of tools, software, and databases--is currently lacking. Thus, here we provide an overview of freely-available, and open-source, tools, algorithms, and frameworks to make both upcoming and established metabolomics researchers aware of the recent developments in an attempt to advance and facilitate data processing workflows in their metabolomics research. The major topics include tools and researches for data processing, data annotation, and data visualization in MS and NMR-based metabolomics. Most in this review described tools are dedicated to untargeted metabolomics workflows; however, some more specialist tools are described as well. All tools and resources described including their analytical and computational platform dependencies are summarized in an overview Table.


Annotation; Data analysis; Data processing; Data visualization; Databases; Mass spectrometry; Metabolites; Metabolomics; NMR; Software tools; Statistics

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