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Data standards can boost metabolomics research, and if there is a will, there is a way
Oxford e-Research Centre, University of Oxford, Oxford, UK.
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK.
National Institute of Genetics, Mishima, Shizuoka, Japan; RIKEN Center for Sustainable Resource Science, Yokohama, Japan.
University of Manchester, Centre for Endocrinology and Diabetes, Manchester, UK; School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, UK.
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2016 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, 14Article in journal (Refereed) Published
Abstract [en]

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little "arm twisting" in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

Place, publisher, year, edition, pages
New York; USA: Springer-Verlag New York, 2016. Vol. 12, 14
Keyword [en]
Data sharing, Data standards, Experimental metadata, Mass spectrometry, Metabolomics, NMR
National Category
Medical and Health Sciences Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:oru:diva-59364DOI: 10.1007/s11306-015-0879-3ISI: 000367426600014PubMedID: 26612985Scopus ID: 2-s2.0-84947561473OAI: oai:DiVA.org:oru-59364DiVA: diva2:1136092
Note

Funding Agencies:

European Commission COSMOS Grant, Projektnr. EC312941

European Commission PhenoMeNal Grant, Projektnr. EC654241

Institut Francais de Bioinformatique (IFB) grant PIA INBS

UK BBSRC, Projektnr. BB/I000933/1, BB/L024152/1, BB/K021125/1

MRC, Projektnr. MR/L01632X/1

BBSRC, Projektnr. BB/L024101/1, BB/J020265/1, BB/L005069/1

University of Oxford e-Research Centre

K BBSRC Grant, Projektnr. BB/L024101/1

Available from: 2017-08-25 Created: 2017-08-25 Last updated: 2017-09-18Bibliographically approved

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