Background: The use of ontologies to control vocabulary and structure annotation has added value to genomescale data, and contributed to the capture and re-use of knowledge across research domains. Gene Ontology (GO) is widely used to capture detailed expert knowledge in genomic-scale datasets and as a consequence has grown to contain many terms, making it unwieldy for many applications. To increase its ease of manipulation and efficiency of use, subsets called GO slims are often created by collapsing terms upward into more general, highlevel terms relevant to a particular context. Creation of a GO slim currently requires manipulation and editing of GO by an expert (or community) familiar with both the ontology and the biological context. Decisions about which terms to include are necessarily subjective, and the creation process itself and subsequent curation are timeconsuming and largely manual. Results: Here we present an objective framework for generating customised ontology slims ...
Melissa J. Davis, Muhammad Shoaib B. Sehgal, Mark