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KEGG Software

KEGG Mapping Tools

KEGG mapping tools developed by Kanehisa Laboratories are available for biological interpretation of genomic, transcriptomic, metabolomic, and other molecular data sets.
KEGG MapperKEGG mapping against PATHWAY/BRITE/MODULE databases, as well as NETWORK/DISEASE databases for human data [reference]
KEGG Web AppsPathway viewer, Brite viewer and Genome browser with mapping capabilities, including advanced coloring of pathway maps

Genome Analysis Tools

Other tools developed by Kanehisa Laboratories include the following:
KEGG SyntaxGenome alignment comparing sequences of KOs for analysis of conserved gene orders in taxonomic groups
KEGG AnnotationOrtholog table and module table tools

Automatic Annotation Servers

The following annotation servers are developed and maintained by Kanehisa Laboratories.
KOALA family tools for automatic annotation of genome and metagenome sequences with subsequent KEGG Mapper analysis [reference]

Other Web Servers

The following web servers are maintained by Kyoto University Bioinformatics Center as part of its GenomeNet service.
KofamKOALAAnother KOALA family tool for automatic KO assignment and KEGG mapping [reference]
KAASThe original KEGG automatic annotation server [reference]
KEGG OCKEGG OC viewer for browsing and analyzing ortholog clusters (OCs) computationally generated from the KEGG SSDB database [reference]
Chemical structure similarity search against KEGG COMPOUND, KEGG DRUG, and other databases. SIMCOMP is based on 2D graph representation, while SUBCOMP is based on bit-string representation of chemical structures [references]
KCaMGlycan structure similarity search against KEGG GLYCAN using tree structure comparison methods [reference]
E-zymeComputational assignment of EC number sub-subclasses from chemical structure transformation patterns of substrates and products [references]
PathPredPrediction of microbial biodegradation pathways and plant second metabolite biosynthesis pathways using reaction patterns in KEGG RCLASS [references]
GENIESGene network prediction from heterogeneous data sets using kernel methods and partially known network information [references]
DINIESDrug-target interaction network prediction from various types of biological data including chemical structures, drug side effects, amino acid sequences and protein domains [references]

The following service is also available at GenomeNet.
Sequence similarity search against KEGG GENES, KEGG GENOME, and other databases.

Last updated: April 1, 2024

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