GO PaD: the Gene Ontology Partition Database

By Gil Alterovitz, Michael Xiang, Mamta Mohan, Marco Ramoni. 2006

73 citations recorded by the source corpus.

Gene Ontology (GO) has been widely used to infer functional significance associated with sets of genes in order to automate discoveries within large-scale genetic studies. A level in GO's direct acyclic graph structure is often assumed to be indicative of its terms' specificities, although other work has suggested this assumption does not hold. Unfortunately, quantitative analysis of biological functions based on nodes at the same level (as is common in gene enrichment analysis tools) can lead to incorrect conclusions as well as missed discoveries due to inefficient use of available information. This paper addresses these using an informational theoretic approach encoded in the GO Partition Database that guarantees to maximize information for gene enrichment analysis. The GO Partition Database was designed to feature ontology partitions with GO terms of similar specificity. The GO partitions comprise varying numbers of nodes and present relevant information theoretic statistics, so researchers can choose to analyze datasets at arbitrary levels of specificity. The GO Partition Database, featuring GO partition sets for functional analysis of genes from human and 10 other commonly studied organisms with a total of 131 972 genes, is available on the internet at: . The site also includes an online tutorial.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

GO Trimming: Systematically reducing redundancy in large Gene Ontology datasets (2011, methodology): “[9] have provided a method to investigate categories across a specified information level of the GO hierarchy by generating a numerical value of the “information content” of each GO term and thus could be used to reduce the size of the output dataset.”
Concept annotation in the CRAFT corpus (2012, background): “…their use in annotations of genes/gene products in model-organism databases (and from this, the information content of these annotations) [62,63], the information content of biomedical concepts can be calculated by analyzing their use in annotations of textual mentions in biomedical…”