Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification

Background: In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant Aspergillus nidulans (mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering. Results: 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 sa...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Irene Kouskoumvekaki, Zhiyong Yang, Svava Óisk Jónsdóttir, Lisbeth Olsson, Gianni Panagiotou
• Keywords: BMCBI 2008, Metabolic, Metabolic Engineering, Metabolite