RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis
1 Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD, 20892, USA
2 Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, 4000 Reservoir Road, NW, Washington, DC 20007, USA
3 SRA International, Inc., Fairfax, VA, USA
4 National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Section on Medical Biophysics, Bethesda, MD 20892, USA
5 University of Maryland, Department of Mathematics, College Park, MD 20742, USA
6 National Institutes of Health, National Eye Institute, Ophthalmic Genetics and Visual Function Branch, Bethesda, MD 20892, USA
7 Departments of Bioinformatics and Computational Biology and Systems Biology, M.D. Anderson Cancer Center, Houston, TX 77030, USA
BMC Bioinformatics 2011, 12:52 doi:10.1186/1471-2105-12-52Published: 10 February 2011
The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ontological analysis of microarray and proteomics studies, typically generating a list of significant functional categories. Two or more of the categories are often redundant, in the sense that identical or nearly-identical sets of genes map to the categories. The redundancy might typically inflate the report of significant categories by a factor of three-fold, create an illusion of an overly long list of significant categories, and obscure the relevant biological interpretation.
We now introduce a new resource, RedundancyMiner, that de-replicates the redundant and nearly-redundant GO categories that had been determined by first running GoMiner. The main algorithm of RedundancyMiner, MultiClust, performs a novel form of cluster analysis in which a GO category might belong to several category clusters. Each category cluster follows a "complete linkage" paradigm. The metric is a similarity measure that captures the overlap in gene mapping between pairs of categories.
RedundancyMiner effectively eliminated redundancies from a set of GO categories. For illustration, we have applied it to the clarification of the results arising from two current studies: (1) assessment of the gene expression profiles obtained by laser capture microdissection (LCM) of serial cryosections of the retina at the site of final optic fissure closure in the mouse embryos at specific embryonic stages, and (2) analysis of a conceptual data set obtained by examining a list of genes deemed to be "kinetochore" genes.