Ontologizer 2.0 - a multifunctional tool for GO term enrichment analysis and data exploration

By Sebastian Bauer, Steffen Grossmann, Martin Vingron, Peter N. Robinson. Bioinform., 2008

536 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fe667462411d45b3ccac8b18fd2e32f55b3cdadc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

In Molecular Biology

Citations in the corpus

GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets (2011, methodology): “This resulted in a large difference in significant terms when compared to the traditional term-for-term method employed in the Ontologizer (175 vs. 236 terms; p-value ≤ 0.1) [19].”
g:Profiler—a web server for functional interpretation of gene lists (2016 update) (2016, unknown): “While the majority of available tools are web services, functional enrichment analysis can be performed using Java applications (18), R packages (10) and Cytoscape plugins (13).”
Visualizing GO Annotations. (2016, background): “The Ontologizer (22) can perform a statistical test for enrichment that accounts for the parent-child redundancy (23) prior to visualizing results.”
The Human Phenotype Ontology (2010, methodology): “In this figure, the results of analysis of a microarray dataset using the Ontologizer (66, 67) are shown, in which GO terms with statistically significant overrepresentation are displayed in green.”
The Gene Ontology of eukaryotic cilia and flagella (2017, methodology): “Bauer S, Grossmann S, Vingron M, Robinson PN. Ontologizer 2.0—a multifunctional tool for GO term enrichment analysis and data exploration.”
EnrichNet: network-based gene set enrichment analysis (2012, methodology): “…et al., 2009), which account for dependencies between genes and proteins inferred from biological networks and ontology graphs (e.g. Ontologizer (Bauer et al., 2008) and GeneCodis (Carmona-Saez et al., 2007)) or by combining multiple types of annotations (e.g. DAVID (Dennis Jr et al., 2003)).”
REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms (2011, methodology): “Ontologizer [23] Standalone Java program that supports multiple statistical approaches for testing for GO term enrichment in a list of genes, while compensating for GO term redundancy due to parent-child relationships, including PCU [24] and MGSA [25] methods.”