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. 2006 Jul 1;34(Web Server issue):W720-4.
doi: 10.1093/nar/gkl167.

KOBAS Server: A Web-Based Platform for Automated Annotation and Pathway Identification

Free PMC article

KOBAS Server: A Web-Based Platform for Automated Annotation and Pathway Identification

Jianmin Wu et al. Nucleic Acids Res. .
Free PMC article


There is an increasing need to automatically annotate a set of genes or proteins (from genome sequencing, DNA microarray analysis or protein 2D gel experiments) using controlled vocabularies and identify the pathways involved, especially the statistically enriched pathways. We have previously demonstrated the KEGG Orthology (KO) as an effective alternative controlled vocabulary and developed a standalone KO-Based Annotation System (KOBAS). Here we report a KOBAS server with a friendly web-based user interface and enhanced functionalities. The server can support input by nucleotide or amino acid sequences or by sequence identifiers in popular databases and can annotate the input with KO terms and KEGG pathways by BLAST sequence similarity or directly ID mapping to genes with known annotations. The server can then identify both frequent and statistically enriched pathways, offering the choices of four statistical tests and the option of multiple testing correction. The server also has a 'User Space' in which frequent users may store and manage their data and results online. We demonstrate the usability of the server by finding statistically enriched pathways in a set of upregulated genes in Alzheimer's Disease (AD) hippocampal cornu ammonis 1 (CA1). KOBAS server can be accessed at


Figure 1
Figure 1
Screenshot of the output of KO annotation when the input is FASTA sequences. The 21 of the 36 upregulated genes in AD CA1 were assigned KO terms based on sequence similarity. Each row corresponds to a query DNA or protein input by the user. The first column contains sequence identifiers extracted from the input. The second column contains the assigned KO terms hyperlinked to detailed descriptions in KEGG. The third column contains KO term definitions. The fourth to seventh columns show the rank, E-value, score and identity of the BLAST hit. The last column contains the gene ID of the hit hyperlinked to the KEGG GENES database. Users can choose to view the results in HTML or text format, edit the text format online and download results to local disks. Users can also select the program for further analysis using the annotation results as input directly.
Figure 2
Figure 2
Screenshot of the list of statistically enriched pathways identified in the upregulated genes in AD CA1, sorted by increasing q-value. The first column shows the name of the pathway. The second column lists the number and percentage of input genes or proteins involved in the pathway (top) and the number and percentage of background genes or proteins involved in the pathway. The third and fourth columns list the p-value and q-value of the statistical significance, respectively.
Figure 3
Figure 3
Screenshot of User Space. Users can organize their data and results in a tree-like structure. Users can upload files from their local disk to the KOBAS server and use them later as input. The output of any analysis will be automatically stored in the User Space for further analysis.

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    1. Ashburner M., Ball C.A., Blake J.A., Botstein D., Butler H., Cherry J.M., Davis A.P., Dolinski K., Dwight S.S., Eppig J.T., et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nature Genet. 2000;25:25–29. - PMC - PubMed
    1. Martin D.M., Berriman M., Barton G.J. GOtcha: a new method for prediction of protein function assessed by the annotation of seven genomes. BMC Bioinformatics. 2004;5:178. - PMC - PubMed
    1. Khan S., Situ G., Decker K., Schmidt C.J. GoFigure: automated Gene Ontology annotation. Bioinformatics. 2003;19:2484–2485. - PubMed
    1. Al-Shahrour F., Diaz-Uriarte R., Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics. 2004;20:578–580. - PubMed
    1. Masseroli M., Martucci D., Pinciroli F. GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res. 2004;32:W293–W300. - PMC - PubMed

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