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GO PaD: the Gene Ontology Partition Database
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...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1669720/ https://www.ncbi.nlm.nih.gov/pubmed/17098937 http://dx.doi.org/10.1093/nar/gkl799 |
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author | Alterovitz, Gil Xiang, Michael Mohan, Mamta Ramoni, Marco F. |
author_facet | Alterovitz, Gil Xiang, Michael Mohan, Mamta Ramoni, Marco F. |
author_sort | Alterovitz, Gil |
collection | PubMed |
description | 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. |
format | Text |
id | pubmed-1669720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-16697202007-02-22 GO PaD: the Gene Ontology Partition Database Alterovitz, Gil Xiang, Michael Mohan, Mamta Ramoni, Marco F. Nucleic Acids Res Articles 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. Oxford University Press 2007-01 2006-11-10 /pmc/articles/PMC1669720/ /pubmed/17098937 http://dx.doi.org/10.1093/nar/gkl799 Text en © 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. |
spellingShingle | Articles Alterovitz, Gil Xiang, Michael Mohan, Mamta Ramoni, Marco F. GO PaD: the Gene Ontology Partition Database |
title | GO PaD: the Gene Ontology Partition Database |
title_full | GO PaD: the Gene Ontology Partition Database |
title_fullStr | GO PaD: the Gene Ontology Partition Database |
title_full_unstemmed | GO PaD: the Gene Ontology Partition Database |
title_short | GO PaD: the Gene Ontology Partition Database |
title_sort | go pad: the gene ontology partition database |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1669720/ https://www.ncbi.nlm.nih.gov/pubmed/17098937 http://dx.doi.org/10.1093/nar/gkl799 |
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