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Review of emerging metabolomic tools and resources: 2015–2016
Author(s) -
Misra Biswapriya B.,
Fahrmann Johannes F.,
Grapov Dmitry
Publication year - 2017
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.201700110
Subject(s) - metabolomics , computer science , petabyte , data science , visualization , systems biology , software , big data , data mining , computational biology , bioinformatics , biology , programming language
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.