Improved detection of overrepresentation of Gene-Ontology annotations with parent-child analysis

By Steffen Grossmann, Sebastian Bauer, Peter N. Robinson, Martin Vingron. Bioinform., 2007

360 citations recorded by the source corpus.

MOTIVATION High-throughput experiments such as microarray hybridizations often yield long lists of genes found to share a certain characteristic such as differential expression. Exploring Gene Ontology (GO) annotations for such lists of genes has become a widespread practice to get first insights into the potential biological meaning of the experiment. The standard statistical approach to measuring overrepresentation of GO terms cannot cope with the dependencies resulting from the structure of GO because they analyze each term in isolation. Especially the fact that annotations are inherited from more specific descendant terms can result in certain types of false-positive results with potentially misleading biological interpretation, a phenomenon which we term the inheritance problem. RESULTS We present here a novel approach to analysis of GO term overrepresentation that determines overrepresentation of terms in the context of annotations to the term's parents. This approach reduces the dependencies between the individual term's measurements, and thereby avoids producing false-positive results owing to the inheritance problem. ROC analysis using study sets with overrepresented GO terms showed a clear advantage for our approach over the standard algorithm with respect to the inheritance problem. Although there can be no gold standard for exploratory methods such as analysis of GO term overrepresentation, analysis of biological datasets suggests that our algorithm tends to identify the core GO terms that are most characteristic of the dataset being analyzed.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/2a2de16f65ab7384ae7567b88b26e20b63fa1d81

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Citations in the corpus

Cellular stress conditions are reflected in the protein and RNA content of endothelial cell-derived exosomes (2012, methodology): “Gene ontology and pathway enrichment analysis Gene Ontology Term for Term analysis was performed using Ontologizer (32,33).”
GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets (2011, methodology): “These methods are more directed towards decorrelating terms from each other so as to minimize the effect of genes being inherited through the hierarchy and to reduce false positives [4].”
Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation (2010, methodology): “Ontologizer [22] defines a modified Fisher’s Exact Test for hierarchical vocabularies, termed the parent-child approach .”
The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. (2008, background): “Additionally,anum-ber of computational algorithms have been developed for ontological analysis after the success of GO (e.g., 19,24,25 ) and can now be applied to human phenotype data.”
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.”