Data processing and interpretation represent the
most challenging and time-consuming steps in high-throughput metabolomic
experiments, regardless of the analytical platform (mass spectrometry
[MS] or nuclear magnetic resonance spectroscopy [NMR]-based) used for
data acquisition. Improved machinery in metabolomics generate
increasingly complex data sets which 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, 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.http://onlinelibrary.wiley.com/doi/10.1002/elps.201500417/abstract
