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KEGG Pathway Analysis

BioCodeKb - Bioinformatics Knowledgebase

The progress in structural genomics will soon uncover the complete genome sequences for hundreds and thousands of organisms. However, roughly a half of the genes identified in every genome thus far sequenced still remain unknown in terms of their biological functions. New experimental and informatics technologies in functional genomics are urgently required for systematic identification of gene functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) is an effort to computerize the current knowledge of biochemical pathways and other types of molecular interactions that can be used as reference for systematic interpretation of sequence data. At the same time, KEGG attempts to standardize the functional annotation of genes and proteins, and maintain gene catalogs for all complete genomes and some partial genomes including mouse and human.

The basic concepts of KEGG and underlying informatics technologies have already been published. KEGG is tightly integrated with the LIGAND chemical database for enzyme reactions as well as with most of the major molecular biology databases by the DBGET/LinkDB system under the Japanese GenomeNet service. The database organization efforts require extensive analyses of completely sequenced genomes.

The KEGG reference maps for metabolic pathways show biochemical knowledge containing all chemically identified reaction pathways. The constraint of the genome, such as a list of enzymes encoded in the genome, will reconstruct organism specific pathways, which are represented by coloring of boxes in the KEGG pathway maps. Furthermore, the constraint of operons will often predict functional units or conserved pathway motifs, which are represented by additional coloring in the KEGG pathway maps. The operon information probably shows a regulatory unit of transcription. Thus, it is easy to see how the information of gene expression profiles can be used as still another constraint against the KEGG reference pathway maps. In fact, KEGG provides a tool to color the pathway maps in order to visualize, for example, the microarray patterns of gene expression profiles.

The user-interfaces for the available tools can be accessed from the KEGG table of contents page. The pathway reconstruction tools in the category of prediction tools are based on sequence similarity search that involves a set of query sequences at a time to see if a pathway is correctly reconstructed. At the moment, there are two versions of the reconstruction tools. One is to search against the KEGG pathway maps and the other is to search against the ortholog group tables. The former contains a larger data set of pathways but the search is made against single organisms. The latter is limited to selected portions of the pathways (pathway motifs or functional units), but the search is made against multiple alignments of organisms and tends to produce better results.

Analyzing GO terms one by one is difficult in KEGG. So only most important terms with rating scores larger than 0.5 for detailed analysis are selected. GO terms can be clustered into three groups: cellular component, molecular function, and biological process.

KAAS (KEGG Automatic Annotation Server) provides functional annotation of genes by BLAST or GHOST comparisons against the manually curated KEGG GENES database. The result contains KO (KEGG Orthology) assignments and automatically generated KEGG pathways.

Blast2GO already supports InterPro, enzyme codes, KEGG pathways, GO direct acyclic graphs (DAGs), and GOSlim.  So it looks like we can perform all of our analysis just by using it.

GO analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were conducted to identify DEGs at the biologically functional level. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to integrate functional genomic annotations was considered to indicate a statistically significant differences.

Stages in Pathway Analysis

  • 1st Stage Analysis: Data Driven Objective (DDO) is used mainly in determining relationship information of genes or proteins identified in a specific experiment (e.g. microarray study)

  • 2nd Stage Analysis: Knowledge Driven Objective (KDO) is used mainly in developing a comprehensive pathway knowledge base for a particular domain of interest (e.g. cell type, disease, system)

  • Repeat 1st Stage after generating new leads and hypothesis

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We are providing “KEGG Pathway Analysis” service to the bioinformatics community through our expertise for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies.


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