The Neural/Immune Gene Ontology: clipping the Gene Ontology for neurological and immunological systems
Department of Microbiology and Immunology, Faculty of Medical Sciences and The National Institute of Biotechnology in the Negev, Ben Gurion University, Beer Sheva, Israel
BMC Bioinformatics 2010, 11:458 doi:10.1186/1471-2105-11-458Published: 12 September 2010
The Gene Ontology (GO) is used to describe genes and gene products from many organisms. When used for functional annotation of microarray data, GO is often slimmed by editing so that only higher level terms remain. This practice is designed to improve the summarizing of experimental results by grouping high level terms and the statistical power of GO term enrichment analysis.
Here, we propose a new approach to editing the gene ontology, clipping, which is the editing of GO according to biological relevance. Creation of a GO subset by clipping is achieved by removing terms (from all hierarchal levels) if they are not functionally relevant to a given domain of interest. Terms that are located in levels higher to relevant terms are kept, thus, biologically irrelevant terms are only removed if they are not parental to terms that are relevant.
Using this approach, we have created the Neural-Immune Gene Ontology (NIGO) subset of GO directed for neurological and immunological systems. We tested the performance of NIGO in extracting knowledge from microarray experiments by conducting functional analysis and comparing the results to those obtained using the full GO and a generic GO slim. NIGO not only improved the statistical scores given to relevant terms, but was also able to retrieve functionally relevant terms that did not pass statistical cutoffs when using the full GO or the slim subset.
Our results validate the pipeline used to generate NIGO, suggesting it is indeed enriched with terms that are specific to the neural/immune domains. The results suggest that NIGO can enhance the analysis of microarray experiments involving neural and/or immune related systems. They also directly demonstrate the potential such a domain-specific GO has in generating meaningful hypotheses.