Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary.
BACKGROUND
High-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation of large sets of genes, including whole genomes, and automated identification of pathways. Ontologies, such as the popular Gene Ontology (GO), provide a common controlled vocabulary for these types of automated analysis. Yet, while GO offers tremendous value, it also has certain limitations such as the lack of direct association with pathways.


RESULTS
We demonstrated the use of the KEGG Orthology (KO), part of the KEGG suite of resources, as an alternative controlled vocabulary for automated annotation and pathway identification. We developed a KO-Based Annotation System (KOBAS) that can automatically annotate a set of sequences with KO terms and identify both the most frequent and the statistically significantly enriched pathways. Results from both whole genome and microarray gene cluster annotations with KOBAS are comparable and complementary to known annotations. KOBAS is a freely available stand-alone Python program that can contribute significantly to genome annotation and microarray analysis.
Mao
Xizeng
X
Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University Beijing 100871, PR China.
Cai
Tao
T
Olyarchuk
John G
JG
Wei
Liping
L
D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D003628:Database Management Systems; D004282:Documentation / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009323:Natural Language Processing; D020543:Proteome / Q000145:classification* / Q000378:metabolism*; D017421:Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D018875:Vocabulary, Controlled
D020543:Proteome