ViPTree is a web server provided through GenomeNet to generate viral proteomic trees for classification of viruses based on genome-wide similarities. Users can upload viral genomes sequenced either by genomics or metagenomics. ViPTree generates proteomic trees for the uploaded genomes together with flexibly selected reference viral genomes. ViPTree also serves as a platform to visually investigate genomic alignments and automatically annotated gene functions for the uploaded viral genomes, thus providing virus researchers the first choice for classifying and understanding newly sequenced viral genomes. ViPTree is freely available at: http://www.genome.jp/viptree. Supplementary data are available at Bioinformatics online.

Environmental genomics can describe all forms of organisms—cellular and viral—present in a community. The analysis of such eco-systems biology data relies heavily on reference databases, e.g., taxonomy or gene function databases. Reference databases of symbiosis sensu lato, although essential for the analysis of organism interaction networks, are lacking. By mining existing databases and literature, we here provide a comprehensive and manually curated database of taxonomic links between viruses and their cellular hosts.

LIGAND is a composite database comprising three sections: COMPOUND for the information about metabolites and other chemical compounds, REACTION for the collection of substrate-product relations representing metabolic and other reactions, and ENZYME for the information about enzyme molecules. The current release (as of September 7, 2001) includes 7298 compounds, 5166 reactions and 3829 enzymes. In addition to the keyword search provided by the DBGET/LinkDB system, a substructure search to the COMPOUND and REACTION sections is now available through the World Wide Web (http://www.genome.ad.jp/ligand/). LIGAND may be also downloaded by anonymous FTP (ftp://ftp.genome.ad.jp/pub/kegg/ligand/).

Motivation: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. Results: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles. Supplementary information: Datasets and all results are available at http://web.kuicr.kyoto-u.ac.jp/supp/smizutan/target-effect/. Availability: Software is available at the above supplementary website. Contact:yamanishi@bioreg.kyushu-u.ac.jp, or goto@kuicr.kyoto-u.ac.jp

One of the greatest challenges in bioinformatics is to shed light on the relationship between genomic and chemical significances of metabolic pathways. Here, we demonstrate two types of chemical structure search servers: SIMCOMP (http://www.genome.jp/tools/simcomp/) for the chemical similarity search and SUBCOMP (http://www.genome.jp/tools/subcomp/) for the chemical substructure search, where both servers provide links to the KEGG PATHWAY and BRITE databases. The SIMCOMP is a graph-based method for searching the maximal common subgraph isomorphism by finding the maximal cliques in the association graph. In contrast, the SUBCOMP is an extended method for solving the subgraph isomorphism problem. The obtained links to PATHWAY or BRITE databases can be used to interpret as the biological meanings of chemical structures from the viewpoint of the various biological functions including metabolic networks.