Show simple item record Allen, Jess Davey, Hazel Marie Broadhurst, David Iain Heald, Jim K. Rowland, Jeremy John Oliver, Stephen G. Kell, Douglas B. 2008-12-16T15:20:18Z 2008-12-16T15:20:18Z 2003-05-12
dc.identifier.citation Allen , J , Davey , H M , Broadhurst , D I , Heald , J K , Rowland , J J , Oliver , S G & Kell , D B 2003 , ' High-throughput classification of yeast mutants for functional genomics using metabolic footprinting ' Nature Biotechnology , vol 21 , pp. 692-696 . DOI: 10.1038/nbt823 en
dc.identifier.issn 1546-1696
dc.identifier.other PURE: 93768
dc.identifier.other PURE UUID: 87716d54-7de8-4884-a3e4-2cd376a700d7
dc.identifier.other dspace: 2160/1727
dc.identifier.other DSpace_20121128.csv: row: 1293
dc.identifier.other Scopus: 0038699591
dc.identifier.uri en
dc.description Jess Allen, Hazel M Davey, David Broadhurst, Jim K Heald, Jem J Rowland, Stephen G Oliver & Douglas B Kell (2003). High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nature Biotechnology, 21 (6), 692-696. Sponsorship: BBSRC / Wellcome Trust RAE2008 en
dc.description.abstract Many technologies have been developed to help explain the function of genes discovered by systematic genome sequencing. At present, transcriptome and proteome studies dominate large-scale functional analysis strategies. Yet the metabolome, because it is 'downstream', should show greater effects of genetic or physiological changes and thus should be much closer to the phenotype of the organism. We earlier presented a functional analysis strategy that used metabolic fingerprinting to reveal the phenotype of silent mutations of yeast genes1. However, this is difficult to scale up for high-throughput screening. Here we present an alternative that has the required throughput (2 min per sample). This 'metabolic footprinting' approach recognizes the significance of 'overflow metabolism' in appropriate media. Measuring intracellular metabolites is time-consuming and subject to technical difficulties caused by the rapid turnover of intracellular metabolites and the need to quench metabolism and separate metabolites from the extracellular space. We therefore focused instead on direct, noninvasive, mass spectrometric monitoring of extracellular metabolites in spent culture medium. Metabolic footprinting can distinguish between different physiological states of wild-type yeast and between yeast single-gene deletion mutants even from related areas of metabolism. By using appropriate clustering and machine learning techniques, the latter based on genetic programming, we show that metabolic footprinting is an effective method to classify 'unknown' mutants by genetic defect. en
dc.format.extent 5 en
dc.language.iso eng
dc.relation.ispartof Nature Biotechnology en
dc.rights en
dc.title High-throughput classification of yeast mutants for functional genomics using metabolic footprinting en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en

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