Detecting a difference - assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants

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dc.contributor.author Enot, David P.
dc.contributor.author Beckmann, Manfred
dc.contributor.author Draper, John
dc.date.accessioned 2009-11-30T10:13:12Z
dc.date.available 2009-11-30T10:13:12Z
dc.date.issued 2007-09
dc.identifier.citation Enot , D P , Beckmann , M & Draper , J 2007 , ' Detecting a difference - assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants ' Metabolomics , vol 3 , no. 3 , pp. 335-347 . , 10.1007/s11306-007-0064-4 en
dc.identifier.issn 1573-3890
dc.identifier.other PURE: 140873
dc.identifier.other dspace: 2160/3719
dc.identifier.uri http://hdl.handle.net/2160/3719
dc.description Enot, D. P., Beckmann, M., Draper, J. (2007). Detecting a difference - assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants. Metabolomics, 3 (3), 335-347. Sponsorship: UK Foods Standards Agency en
dc.description.abstract There is current debate on whether genetically-manipulated plants might contain unexpected, potentially undesirable, changes in overall metabolite composition relative to that of the progenitor genotype. However, appropriate analytical technology and acceptable metrics of compositional similarity require development, particularly to allow data integration from different laboratories and different harvests. For an initial comprehensive overview of compositional similarity, we explored the use of a rapid and relatively non-selective fingerprinting technique based on flow injection electrospray ionisation mass spectrometry (FIE-MS). Six conventionally-bred potato cultivars and six experimental bioengineered potato genotypes were produced in four field blocks during two growing seasons and analysed on two different analytical instruments (LCT, Micromass in 2001 and LTQ, Thermo Finnigan in 2003). Field effects and overall process variability was found to be negligible when compared to inherited genotype variance. The data derived separately for experiments using tubers from individual harvest years were compared to assess the generalisability of models for the comparison of GM and non-GM potato tubers under investigation. This procedure proved appropriate for not only rapid assessment of similarities between plant genotypes but also to predict the identity of metabolite signals that could explain differences between genotype classes irrespective of the instrument used for analysis. Importantly, despite differences in ionisation and data acquisition properties of the two instruments the generalisation of models could be confirmed after correlation analysis of explanatory variables correctly identified the molecular origin of differences between genotypes. We conclude that FIE-MS metabolomics fingerprinting technology coupled to machine learning data analysis has great potential as a robust tool for first-pass metabolic phenotyping and, therefore, initial assessments of compositional similarities prior to use of more targeted hyphenated gas or liquid chromatography-mass spectrometry techniques. en
dc.format.extent 13 en
dc.language.iso eng
dc.relation.ispartof Metabolomics en
dc.title Detecting a difference - assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1007/s11306-007-0064-4
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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