Analysis of metabolite profile data using batch-learning self-organizing maps

Novel tools are needed for efficient analysis and visualization of the massive data sets associated with metabolomics. Here, we describe a batch-learning self-organizing map (BL-SOM) for metabolome informatics that makes the learning process and resulting map independent of the order of data input. This approach was successfully used in analyzing and organizing the metabolome data forArabidopsis thaliana cells cultured under salt stress. Our 6 × 4 matrix presented patterns of metabolite levels at different time periods. A negative correlation was found between the levels of amino acids and metabolites related to glycolysis metabolism in response to this stress. Therefore, BL-SOM could be an excellent tool for clustering and visualizing high dimensional, complex metabolome data in a single map.

[1]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[2]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[3]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[4]  O. Fiehn,et al.  Metabolite profiling for plant functional genomics , 2000, Nature Biotechnology.

[5]  Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. , 2001, The Plant cell.

[6]  S. Kanaya,et al.  Analysis of codon usage diversity of bacterial genes with a self-organizing map (SOM): characterization of horizontally transferred genes with emphasis on the E. coli O157 genome. , 2001, Gene.

[7]  Daniel Kost,et al.  Low-frequency electromagnetic fields induce a stress effect upon higher plants, as evident by the universal stress signal, alanine. , 2003, Biochemical and biophysical research communications.

[8]  R. Dixon,et al.  Plant metabolomics: large-scale phytochemistry in the functional genomics era. , 2003, Phytochemistry.

[9]  Lothar Willmitzer,et al.  De Novo Amino Acid Biosynthesis in Potato Tubers Is Regulated by Sucrose Levels1[w] , 2003, Plant Physiology.

[10]  Shigehiko Kanaya,et al.  Informatics for unveiling hidden genome signatures. , 2003, Genome research.

[11]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[12]  O. Fiehn.  Metabolomics – the link between genotypes and phenotypes , 2004, Plant Molecular Biology.

[13]  M. Hirai,et al.  Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[14]  E. Fukusaki,et al.  Plant metabolomics: potential for practical operation. , 2005, Journal of bioscience and bioengineering.

[15]  Yury Tikunov,et al.  A Novel Approach for Nontargeted Data Analysis for Metabolomics. Large-Scale Profiling of Tomato Fruit Volatiles1[w] , 2005, Plant Physiology.

[16]  S. Kanaya,et al.  Self-Organizing Map (SOM) unveils and visualizes hidden sequence characteristics of a wide range of eukaryote genomes. , 2006, Gene.

[17]  J. K. Kim,et al.  Time-course metabolic profiling in Arabidopsis thaliana cell cultures after salt stress treatment. , 2007, Journal of experimental botany.