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GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists
Phenotype analysis is commonly recognized to be of great importance for gaining insight into genetic interaction underlying inherited diseases. However, few computational contributions have been proposed for this purpose, mainly owing to lack of controlled clinical information easily accessible and...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2005
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160215/ https://www.ncbi.nlm.nih.gov/pubmed/15980570 http://dx.doi.org/10.1093/nar/gki454 |
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author | Masseroli, Marco Galati, Osvaldo Pinciroli, Francesco |
author_facet | Masseroli, Marco Galati, Osvaldo Pinciroli, Francesco |
author_sort | Masseroli, Marco |
collection | PubMed |
description | Phenotype analysis is commonly recognized to be of great importance for gaining insight into genetic interaction underlying inherited diseases. However, few computational contributions have been proposed for this purpose, mainly owing to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. We developed and made available through GFINDer web server an original approach for the analysis of genetic disorder related genes by exploiting the information on genetic diseases and their clinical phenotypes present in textual form within the Online Mendelian Inheritance in Man (OMIM) database. Because several synonyms for the same name and different names for overlapping concepts are often used in OMIM, we first normalized phenotype location descriptions reducing them to a list of unique controlled terms representing phenotype location categories. Then, we hierarchically structured them and the correspondent genetic diseases according to their topology and granularity of description, respectively. Thus, in GFINDer we could implement specific Genetic Disorders modules for the analysis of these structured data. Such modules allow to automatically annotate user-classified gene lists with updated disease and clinical information, classify them according to the genetic syndrome and the phenotypic location categories, and statistically identify the most relevant categories in each gene class. GFINDer is available for non-profit use at . |
format | Text |
id | pubmed-1160215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2005 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-11602152005-06-29 GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists Masseroli, Marco Galati, Osvaldo Pinciroli, Francesco Nucleic Acids Res Article Phenotype analysis is commonly recognized to be of great importance for gaining insight into genetic interaction underlying inherited diseases. However, few computational contributions have been proposed for this purpose, mainly owing to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. We developed and made available through GFINDer web server an original approach for the analysis of genetic disorder related genes by exploiting the information on genetic diseases and their clinical phenotypes present in textual form within the Online Mendelian Inheritance in Man (OMIM) database. Because several synonyms for the same name and different names for overlapping concepts are often used in OMIM, we first normalized phenotype location descriptions reducing them to a list of unique controlled terms representing phenotype location categories. Then, we hierarchically structured them and the correspondent genetic diseases according to their topology and granularity of description, respectively. Thus, in GFINDer we could implement specific Genetic Disorders modules for the analysis of these structured data. Such modules allow to automatically annotate user-classified gene lists with updated disease and clinical information, classify them according to the genetic syndrome and the phenotypic location categories, and statistically identify the most relevant categories in each gene class. GFINDer is available for non-profit use at . Oxford University Press 2005-07-01 2005-06-27 /pmc/articles/PMC1160215/ /pubmed/15980570 http://dx.doi.org/10.1093/nar/gki454 Text en © The Author 2005. Published by Oxford University Press. All rights reserved |
spellingShingle | Article Masseroli, Marco Galati, Osvaldo Pinciroli, Francesco GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists |
title | GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists |
title_full | GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists |
title_fullStr | GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists |
title_full_unstemmed | GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists |
title_short | GFINDer: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists |
title_sort | gfinder: genetic disease and phenotype location statistical analysis and mining of dynamically annotated gene lists |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1160215/ https://www.ncbi.nlm.nih.gov/pubmed/15980570 http://dx.doi.org/10.1093/nar/gki454 |
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