Literature DB >> 24656939

metaMS: an open-source pipeline for GC-MS-based untargeted metabolomics.

Ron Wehrens1, Georg Weingart2, Fulvio Mattivi2.   

Abstract

Untargeted metabolomics are rapidly becoming an important tool for studying complex biological samples. Gas chromatography-mass spectrometry (GC-MS) is the most widely used analytical technology for metabolomic analysis of compounds that are volatile or can be chemically derivatised into volatile compounds. Unfortunately, data processing and analysis are not straightforward and the field is dominated by vendor-supplied software that does not always allow easy integration for large laboratories with different instruments. This paper presents an open-source pipeline for high-throughput GC-MS data processing, written in the R language and available as package metaMS. It features rapid annotation using in-house databases, and also provides support for building and validating such databases. The results are presented in simple-to-use tables, summarising the relative concentrations of identified compounds and unknowns in all samples. The use of the pipeline is illustrated using three experimental data sets.
Copyright © 2014 Elsevier B.V. All rights reserved.

Keywords:  Annotation; GC–MS; Metabolomics; Open-source software; Volatiles

Mesh:

Year:  2014        PMID: 24656939     DOI: 10.1016/j.jchromb.2014.02.051

Source DB:  PubMed          Journal:  J Chromatogr B Analyt Technol Biomed Life Sci        ISSN: 1570-0232            Impact factor:   3.205


  19 in total

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Review 4.  Review of recent developments in GC-MS approaches to metabolomics-based research.

Authors:  David J Beale; Farhana R Pinu; Konstantinos A Kouremenos; Mahesha M Poojary; Vinod K Narayana; Berin A Boughton; Komal Kanojia; Saravanan Dayalan; Oliver A H Jones; Daniel A Dias
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Journal:  Environ Sci Pollut Res Int       Date:  2016-04-06       Impact factor: 4.223

Review 6.  Low-cost and open-source strategies for chemical separations.

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Journal:  Front Bioeng Biotechnol       Date:  2014-12-16

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9.  Microbiota functional activity biosensors for characterizing nutrient metabolism in vivo.

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Review 10.  Navigating freely-available software tools for metabolomics analysis.

Authors:  Rachel Spicer; Reza M Salek; Pablo Moreno; Daniel Cañueto; Christoph Steinbeck
Journal:  Metabolomics       Date:  2017-08-09       Impact factor: 4.290

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