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 Mapper | KEGG mapping against PATHWAY/BRITE/MODULE databases, as well as NETWORK/DISEASE databases for human data [reference] |
| KEGG Web Apps | Pathway 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 Syntax | Genome alignment comparing sequences of KOs for analysis of conserved gene orders in taxonomic groups |
| KEGG Annotation | Ortholog table and module table tools |
Automatic Annotation Servers
The following annotation servers are developed and maintained by Kanehisa Laboratories.| BlastKOALA GhostKOALA | 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.| KofamKOALA | Another KOALA family tool for automatic KO assignment and KEGG mapping [reference] |
| KAAS | The original KEGG automatic annotation server [reference] |
| KEGG OC | KEGG OC viewer for browsing and analyzing ortholog clusters (OCs) computationally generated from the KEGG SSDB database [reference] |
| SIMCOMP SUBCOMP | 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] |
| KCaM | Glycan structure similarity search against KEGG GLYCAN using tree structure comparison methods [reference] |
| E-zyme | Computational assignment of EC number sub-subclasses from chemical structure transformation patterns of substrates and products [references] |
| PathPred | Prediction of microbial biodegradation pathways and plant second metabolite biosynthesis pathways using reaction patterns in KEGG RCLASS [references] |
| GENIES | Gene network prediction from heterogeneous data sets using kernel methods and partially known network information [references] |
| DINIES | Drug-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.
Last updated: April 1, 2024