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Published on April 27, 2007; 10.1104/pp.106.095059

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Received December 19, 2006
Accepted April 18, 2007

EGENES: Transcriptome-based Plant Database of Genes with Metabolic Pathway Information and EST Indices in KEGG

Ali Masoudi-Nejad *, Susumu Goto , Ruy Jauregui , Masumi Ito , Shuichi Kawashima , Yuki Moriya , Takashi R. Endo , and Minoru Kanehisa

Laboratory of Bioknowledge Systems, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho Uji, Kyoto 611-0011, Japan; Laboratory of Bioinformatics and Bioknowledge Systems. Institute of Biochemistry and Biophysics (IBB), University of Tehran, P.O. Box 13145-1384, Tehran, Iran; Laboratory of Genome Database, Human Genome Center, University of Tokyo, Tokyo 108-8639, Japan; Laboratory of Plant Genetics, Division of Applied Bioscience, Kyoto University, Kyoto 606-8502, Japan

* Corresponding author; email: amasoudin{at}ibb.ut.ac.ir.

EGENES is a knowledge-based database for efficient analysis of plant ESTs that was recently added to the KEGG suite of databases. It links plant genomic information with higher order functional information in a single database. It also provides gene indices for each genome. The genomic information in EGENES is a collection of EST-contigs constructed from assembly of expressed sequence tags (ESTs). Due to the extremely large genomes of plant species, the bulk collection of data such as ESTs is a quick way to capture a complete repertoire of genes expressed in an organism. Using ESTs for reconstructing metabolic pathways is a new expansion in KEGG and provides researchers with a new resource for species in which only EST sequences are available. Functional annotation in EGENES is a process of linking a set of genes/transcripts in each genome with a network of interacting molecules in the cell. EGENES is a multispecies, integrated resource consisting of genomic, chemical, and network information containing a complete set of building blocks (genes and molecules) and wiring diagrams (biological pathways) to represent cellular functions. Using EGENES, genome-based pathway annotation and EST-based annotation can now be compared and mutually validated. The ultimate goals of EGENES will be to bring new plant species into KEGG by clustering and annotating ESTs; to abstract knowledge and principles from large-scale plant EST data; and to improve computational prediction of systems of higher complexity. EGENES will be updated at least once a year. EGENES is publicly available and is accessible by following link or by KEGG's navigation system (http://www.genome.jp/kegg-bin/create_kegg_menu?category=plants_egenes).




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