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RedundancyMiner: De-replication of redundant GO categories in microarray and proteomics analysis
Barry R Zeeberg1, Hongfang Liu, Ari B Kahn
1Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Room 5068, Building 37, 37 Convent Drive, Bethesda, MD 20892, USA. barry@discover.nci.nih.gov
BMC Bioinformatics
|February 12, 2011
Summary
RedundancyMiner streamlines Gene Ontology (GO) analysis by removing redundant categories. This tool enhances biological interpretation of microarray and proteomics studies by presenting a clearer, more focused list of significant GO terms.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- The Gene Ontology (GO) organizes genes into hierarchical categories for biological analysis.
- Existing tools like GoMiner can produce redundant GO categories, obscuring biological interpretation.
- Redundancy inflates significant category lists and can mislead researchers.
Purpose of the Study:
- To introduce RedundancyMiner, a novel resource for de-replicating GO categories.
- To improve the clarity and biological interpretability of GO-based analyses.
Main Methods:
- RedundancyMiner utilizes a novel clustering algorithm called MultiClust.
- MultiClust employs a complete linkage paradigm and a similarity metric based on gene mapping overlap.
- The algorithm identifies and removes redundant or near-redundant GO categories.
Main Results:
- RedundancyMiner effectively eliminates redundancies from sets of GO categories.
- The tool was applied to clarify results from gene expression studies and conceptual datasets.
Conclusions:
- RedundancyMiner significantly enhances the interpretation of GO-based analyses.
- The tool provides a more focused and biologically relevant output for researchers.
