| Literature DB >> 18160330 |
Kazuki Saito1, Masami Y Hirai, Keiko Yonekura-Sakakibara.
Abstract
Following the sequencing of whole genomes of model plants, high-throughput decoding of gene function is a major challenge in modern plant biology. In view of remarkable technical advances in transcriptomics and metabolomics, integrated analysis of these 'omics' by data-mining informatics is an excellent tool for prediction and identification of gene function, particularly for genes involved in complicated metabolic pathways. The availability of Arabidopsis public transcriptome datasets containing data of >1000 microarrays reinforces the potential for prediction of gene function by transcriptome coexpression analysis. Here, we review the strategy of combining transcriptome and metabolome as a powerful technology for studying the functional genomics of model plants and also crop and medicinal plants.Entities:
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Year: 2007 PMID: 18160330 DOI: 10.1016/j.tplants.2007.10.006
Source DB: PubMed Journal: Trends Plant Sci ISSN: 1360-1385 Impact factor: 18.313