%0 Journal Article
%T Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification
%A Irene Kouskoumvekaki
%A Zhiyong Yang
%A Svava 車 J車nsd車ttir
%A Lisbeth Olsson
%A Gianni Panagiotou
%J BMC Bioinformatics
%D 2008
%I BioMed Central
%R 10.1186/1471-2105-9-59
%X More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different A. nidulans strains.Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data.Functional genomics approaches are increasingly being used for the elucidation of complex biological questions with applications that range from human health to microbial strain improvement [1-3]. Functional genomics tools have in common that they aim to map the complete phenotypic response of an organism to the environmental conditions of interest. Metabolomics technology is used to identify and quantify the metabolome, which represents the dynamic set of all small molecules 每 excluding those resulting from DNA and RNA transcription or translation 每 present in an organism or a biological sample [4]. Fundamentally, the measured metabolite levels at a defined time under specific culture conditions for a given genotype should reflect a precise and unique signature of the metabolic phenotype [5]. In this sense, the technique is distinct from metabolic profiling, which looks for target compounds identified a priori and their consequent biochemical
%U http://www.biomedcentral.com/1471-2105/9/59