Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function.
Given the ever-increasing number of species for which full-genome sequencing has been realized, there is a rising burden for gene functional annotation. In this study, we provide a detailed protocol that combines co-response gene analysis (using target genes of known function to allow the identification of nonannotated genes likely to be involved in a certain metabolic process) with the identification of target compounds through metabolomics. Strategies exist for applying this information to populations generated by both forward and reverse genetics approaches, although none of these are facile. This approach can also be used as a tool to identify unknown mass-spectral peaks representing new or unusual secondary metabolites, which is currently the major challenge of this analytical research field.
Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany.
Takayuki
T
Tohge
Alisdair R
AR
Fernie
D019295:Computational Biology; D014644:Genetic Variation; D018745:Genome, Plant; D023281:Genomics / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D010944:Plants / Q000235:genetics* / Q000378:metabolism