Background#R##N#Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied.
['Marc Bruckskotten', 'Mario Looso', 'Franz Cemiĉ', 'Anne Konzer', 'Jürgen Hemberger', 'Marcus Krüger', 'Thomas Braun']
PCA2GO: a new multivariate statistics based method to identify highly expressed GO-Terms