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Navigating freely-available software tools for metabolomics analysis

Published version
Peer-reviewed

Type

Article

Change log

Authors

Salek, RM 
Moreno, P 
Cañueto, C 
Steinbeck, C 

Abstract

Introduction The field of metabolomics has expanded greatly over the past two decades, both as an experimen- tal science with applications in many areas, as well as in regards to data standards and bioinformatics software tools. The diversity of experimental designs and instrumental technologies used for metabolomics has led to the need for distinct data analysis methods and the development of many software tools. Objectives To compile a comprehensive list of the most widely used freely available software and tools that are used primarily in metabolomics. Methods The most widely used tools were selected for inclusion in the review by either ≥ 50 citations on Web of Science (as of 08/09/16) or the use of the tool being reported in the recent Metabolomics Society survey. Tools were then categorised by the type of instrumental data (i.e. LC–MS, GC–MS or NMR) and the functionality (i.e. pre- and post- processing, statistical analysis, work ow and other func- tions) they are designed for. Results A comprehensive list of the most used tools was compiled. Each tool is discussed within the context of its application domain and in relation to comparable tools of the same domain. An extended list including additional tools is available at https://github.com/RASpicer/Metabolomics- Tools which is classi ed and searchable via a simple con- trolled vocabulary. Conclusion This review presents the most widely used tools for metabolomics analysis, categorised based on their main functionality. As future work, we suggest a direct com- parison of tools’ abilities to perform specific data analysis tasks e.g. peak picking

Description

Keywords

metabolomics, bioinformatics, software, freely available, data analysis

Journal Title

Metabolomics

Conference Name

Journal ISSN

1573-3882
1573-3890

Volume Title

13

Publisher

Springer Nature
Sponsorship
This work was supported nancially through a BBSRC Grant No. BB/M027635/1; MRC UK MEDical BIOinformatics part- nership, Grant No. MR/L01632X/1; and the PhenoMeNal European Commission’s Horizon2020 programme, Grant Number 654241