To understand a whole mechanism of a living organism by linking a range of information from its genome, transcriptome, proteome, and metabolome, it is essential to have a platform capable of integrating a range of "-omics" data so that they can be analyzed together.
"Batch-learning self-organizing map" (BL-SOM) is one tool that can support such integrated analyses (Kanaya et al.; Abe et al.). The transcriptome and metabolome dataset of this tool consists of a matrix in which signal intensities are arranged in multiple columns (experimental series) and multiple rows (gene and metabolite IDs).
BL-SOM can analyze an integrated matrix of both transcriptome and metabolome data after appropriate normalization of the data and preliminary calculations, allowing researchers to visualize the correlations between elements.
Genes and metabolites are classified into clusters in a two-dimensional "feature map" based on their expression and accumulation patterns. Owing to the high reproducibility and resolution provided by BL-SOM, gene-to-metabolite, gene-to-gene, and metabolite-to-metabolite correlations can be elucidated, facilitating the identification of a gene's function (Hirai et al. 2004; Hirai et al. 2005).
- Kanaya, S., et al. (2001). "Analysis of codon usage diversity for bacterial genes with a self-organizing map (SOM): characterization of horizontally transferred genes with emphasis on the E. coli O157 genome.", Gene, 276, 89–99. [PubMed]
- Abe, T., et al. (2003). "Informatics for unvailing hidden genome signature., Genome Res., 13, 693–702. [PubMed]
- Hirai, M. Y., et al. (2004). "Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana." Proc Natl Acad Sci U S A 101(27): 10205–10210. [PubMed]
- Hirai, M. Y., et al. (2005). "Elucidation of gene-to-gene and metabolite-to-gene networks in arabidopsis by integration of metabolomics and transcriptomics." J Biol Chem 280(27): 25590–25595. [PubMed]
- Search for related genes based on transcriptional profiling
under sulfur- and nitrogen-deficiency conditions
- Search for related genes and metabolites based on transcriptional and metabolic profiling
time-course data under sulfur-deficiency conditions