title: Genome-enabled plant metabolomics.
abstract: The grand challenge currently facing metabolomics is that of comprehensitivity whilst next generation sequencing and advanced proteomics methods now allow almost complete and at least 50% coverage of their respective target molecules, metabolomics platforms at best offer coverage of just 10% of the small molecule complement of the cell. Here we discuss the use of genome sequence information as an enabling tool for peak identity and for translational metabolomics. Whilst we argue that genome information is not sufficient to compute the size of a species metabolome it is highly useful in predicting the occurrence of a wide range of common metabolites. Furthermore, we describe how via gene functional analysis in model species the identity of unknown metabolite peaks can be resolved. Taken together these examples suggest that genome sequence information is current (and likely will remain), a highly effective tool in peak elucidation in mass spectral metabolomics strategies.
authors.affiliation: Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany.
authors.forename: Takayuki
authors.initials: T
authors.lastname: Tohge
authors.affiliation: Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany.
authors.forename: Leonardo Perez
authors.initials: LP
authors.lastname: de Souza
authors.affiliation: Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany. Electronic address: fernie@mpimp-golm.mpg.de.
authors.forename: Alisdair R
authors.initials: AR
authors.lastname: Fernie
mesh_terms: D018745:Genome, Plant; D023281:Genomics / Q000379:methods*; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*; D010944:Plants / Q000235:genetics* / Q000378:metabolism*
keywords: Genomics; Metabolomics; Plant metabolism; Secondary metabolite