CLUGO: A clustering algorithm for automated functional annotations based on gene ontology

We address the issue of providing highly informative and comprehensive annotations using information revealed by the structured vocabularies of Gene Ontology (GO). For a target, a set of candidate terms for inferring tat-get properties is collected and form a unique distribution on the GO directed acyclic graph (DAG). We propose a novel ontology-based clustering algorithm-CLUGO, which considers GO hierarchical characteristics and the clustering of term distributions. By identifying significant groups in the distributions, CLUGO assigns comprehensive and correct annotations for a target. According to the results of experiments with automated sequence functional annotations, CLUGO represents a considerable improvement over our previous work-GOMIT in terms of recall while maintaining a similar level of precision. We conclude that given a GO candidate term distribution, CLUGO is an efficient ontology-based clustering algorithm for selecting comprehensive and correct annotations.