Nucleic Acids Research Advance Access originally published online on November 10, 2006
Nucleic Acids Research 2007 35(Database issue):D322-D327; doi:10.1093/nar/gkl799
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Nucleic Acids Research, 2007, Vol. 35, Database issue D322-D327
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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GO PaD: the Gene Ontology Partition Database
1 Division of Health Sciences and Technology Harvard Medical School and Massachusetts Institute of Technology, Boston, MA 2 Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology Cambridge, MA 3 Children's Hospital Informatics Program Boston, MA 4 Harvard Partners Center for Genetics and Genomics, Harvard Medical School Boston, MA 5 Department of Biology, Massachusetts Institute of Technology Cambridge, MA
*To whom correspondence should be addressed at: New Research Building, Room 250, 77 Avenue Louis Pasteur, Harvard Medical School, Boston, MA 02115, USA. Tel: +1 617 525 4478; Fax: +1 617 525 4488; Email: gil{at}mit.edu
Received August 15, 2006. Revised October 1, 2006. Accepted October 2, 2006.
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: bcl.med.harvard.edu/proj/gopart. The site also includes an online tutorial.
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