GO PaD: the Gene Ontology Partition Database. Export

@article{citeulike:1038497, abstract = {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.}, address = {Division of Health Sciences and Technology Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA. gil@mit.edu}, author = {Alterovitz, G. and Xiang, M. and Mohan, M. and Ramoni, M. F.}, citeulike-article-id = {1038497}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/17098937}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=17098937}, issn = {1362-4962}, journal = {Nucleic Acids Res}, keywords = {go, networks}, month = {January}, number = {Database issue}, posted-at = {2008-08-30 00:53:09}, priority = {3}, title = {GO PaD: the Gene Ontology Partition Database.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17098937}, volume = {35}, year = {2007} }

TY - JOUR ID - citeulike:1038497 L3 - citeulike-article-id:1038497 N2 - 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. IS - Database issue JF - Nucleic Acids Res SN - 1362-4962 AD - Division of Health Sciences and Technology Harvard Medical School and Massachusetts Institute of Technology, Boston, MA, USA. gil@mit.edu TI - GO PaD: the Gene Ontology Partition Database. VL - 35 KW - go KW - networks AU - Alterovitz, G AU - Xiang, M AU - Mohan, M AU - Ramoni, MF PY - 2007/01// UR - http://view.ncbi.nlm.nih.gov/pubmed/17098937 ER -