KEGG and GenomeNet Resources for Predicting Protein Function from Omics Data Including KEGG PLANT Resource
With the rise of experimental technologies for omics research in recent years, considerable quantitative data related to transcription, protein and metabolism are available for predicting protein functions. To predict protein functions from large omics data, reference knowledge databases and bioinformatics tools play considerable roles. KEGG (http://www.genome.jp/kegg/) database we have been establishing is an integrated database of biological systems including genomic, chemical and systemic functional information. Our group has also been developing the tools for genome or chemical analysis as GenomeNet Bioinformatics Tools (http://www.genome.jp/en/gn_tools.html). In this chapter, we introduce the KEGG database resources and the GenomeNet Bioinformatics Tools for predicting protein functions from the viewpoint of omics research, as well as some recent topics (KEGG PLANT Resource and PathPred). KEGG PLANT Resource is one of the contents in the KEGG EDRUG database, and contains links for plant secondary metabolite biosynthesis pathways, plant genomes and EST sequences, chemical information of plant natural products and the prediction tool for plant secondary metabolism pathway. PathPred is a recently developed pathway prediction tool based on the chemical structure transformation patterns of enzyme reactions found in metabolic pathways.
KeywordsKEGG Pathway Plant Secondary Metabolite Crude Drug Enzyme Commission Number Secondary Metabolite Biosynthesis
The computational resources were provided by the Bioinformatics Center, Institute for Chemical Research, Kyoto University. The KEGG project is supported by the Institute for Bioinformatics Research and Development of the Japan Science and Technology Agency, and a grant-in-aid for scientific research on the priority area “Comprehensive Genomics” from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
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