Background: Microarray experiments, as well as other genomic analyses, often result in large gene sets containing up to several hundred genes. The biological significance of such sets of genes is, usually, not readily apparent. Identification of the functions of the genes in the set can help highlight features of interest. The Gene Ontology Consortium 1 has annotated genes in several model organisms using a controlled vocabulary of terms and placed the terms on a Gene Ontology (GO), which comprises three disjoint hierarchies for Molecular functions, Biological processes and Cellular locations. The annotations can be used to identify functions that are enriched in the set, but this analysis can be misleading since the underlying distribution of genes among various functions is not uniform. For example, a large number of genes in a set might be kinases just because the genome contains many kinases.

Results: We use the Gene Ontology hierarchy and the annotations to pick significant functions and pathways by comparing the distribution of functions in a given gene list against the distribution of all the genes in the genome, using the hypergeometric distribution to assign probabilities. GObar is a web-based visualizer that implements this algorithm. The public website for GObar 2 can analyse gene lists from the yeast (S. cervisiae), fly (D. Melanogaster), mouse (M. musculus) and human (H. sapiens) genomes. It also allows visualization of the GO tree, as well as placement of a single gene on the GO hierarchy. We analyse a gene list from a genomic study of pre-mRNA splicing to demonstrate the utility of GObar.

Conclusion: GObar is freely available as a web-based tool at http://katahdin.cshl.org:9331/GO2 and can help analyze and visualize gene lists from genomic analyses.

• PMID: 16042800
• DOI full text
• PMC full text
• PubMed
• PubTator

Reference Type

Journal Article | Research Support, Non-U.S. Gov't

Authors

Lee JS, Katari G, Sachidanandam R

Primary Lit For

Additional Lit For

Review For