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      Navigating freely-available software tools for metabolomics analysis

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          Abstract

          Introduction

          The field of metabolomics has expanded greatly over the past two decades, both as an experimental 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, workflow and other functions) 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/MetabolomicsTools which is classified and searchable via a simple controlled 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 comparison of tools’ abilities to perform specific data analysis tasks e.g. peak picking.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s11306-017-1242-7) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references147

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            PubChem Substance and Compound databases

            PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.
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              MS-DIAL: Data Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis

              Data-independent acquisition (DIA) in liquid chromatography tandem mass spectrometry (LC-MS/MS) provides more comprehensive untargeted acquisition of molecular data. Here we provide an open-source software pipeline, MS-DIAL, to demonstrate how DIA improves simultaneous identification and quantification of small molecules by mass spectral deconvolution. For reversed phase LC-MS/MS, our program with an enriched LipidBlast library identified total 1,023 lipid compounds from nine algal strains to highlight their chemotaxonomic relationships.
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                Author and article information

                Contributors
                +49-3641-948171 , christoph.steinbeck@uni-jena.de
                Journal
                Metabolomics
                Metabolomics
                Metabolomics
                Springer US (New York )
                1573-3882
                1573-3890
                9 August 2017
                9 August 2017
                2017
                : 13
                : 9
                : 106
                Affiliations
                [1 ]ISNI 0000 0000 9709 7726, GRID grid.225360.0, European Molecular Biology Laboratory, , European Bioinformatics Institute (EMBL-EBI), ; Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD UK
                [2 ]ISNI 0000 0001 1939 2794, GRID grid.9613.d, , Friedrich-Schiller-University Jena, ; Lessingstr. 8, Jena, 07743 Germany
                [3 ]ISNI 0000 0001 2284 9230, GRID grid.410367.7, Metabolomics Platform, IISPV, DEEEA, , Universitat Rovira i Virgili, Campus Sescelades, ; Carretera de Valls, s/n, 43007 Tarragona, Catalonia Spain
                Author information
                http://orcid.org/0000-0002-2807-8796
                http://orcid.org/0000-0001-8604-1732
                http://orcid.org/0000-0002-9856-1679
                http://orcid.org/0000-0003-2948-0127
                http://orcid.org/0000-0001-6966-0814
                Article
                1242
                10.1007/s11306-017-1242-7
                5550549
                28890673
                50d1df8b-2b52-4fcf-a398-ad46019907c9
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 11 April 2017
                : 25 July 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award ID: BB/M027635/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/L01632X/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100007601, Horizon 2020;
                Award ID: 654241
                Award Recipient :
                Categories
                Review Article
                Custom metadata
                © Springer Science+Business Media, LLC 2017

                Molecular biology
                metabolomics,bioinformatics,software,freely available,data analysis
                Molecular biology
                metabolomics, bioinformatics, software, freely available, data analysis

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