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Motivation: While the manually curated Gene Ontology (GO) is widely used, inferring a GO directly from -omics data is a compelling new problem. Recognizing that ontologies are a directed acyclic graph (DAG) of terms and hierarchical relations, algorithms are needed that:

• analyze a full matrix of gene–gene pairwise similarities from -omics data;
• infer true hierarchical structure in these data rather than enforcing hierarchy as a computational artifact; and
• respect biological pleiotropy, by which a term in the hierarchy can relate to multiple higher level terms.

Methods addressing these requirements are just beginning to emerge—none has been evaluated for GO inference.

Methods: We consider two algorithms [Clique Extracted Ontology (CliXO), LocalFitness] that uniquely satisfy these requirements, compared with methods including standard clustering. CliXO is a new approach that finds …