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Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System
Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776528/ https://www.ncbi.nlm.nih.gov/pubmed/19956731 http://dx.doi.org/10.1371/journal.pone.0007979 |
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author | Mavromatis, Konstantinos Chu, Ken Ivanova, Natalia Hooper, Sean D. Markowitz, Victor M. Kyrpides, Nikos C. |
author_facet | Mavromatis, Konstantinos Chu, Ken Ivanova, Natalia Hooper, Sean D. Markowitz, Victor M. Kyrpides, Nikos C. |
author_sort | Mavromatis, Konstantinos |
collection | PubMed |
description | Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG's regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov. |
format | Text |
id | pubmed-2776528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-27765282009-12-03 Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System Mavromatis, Konstantinos Chu, Ken Ivanova, Natalia Hooper, Sean D. Markowitz, Victor M. Kyrpides, Nikos C. PLoS One Research Article Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG's regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov. Public Library of Science 2009-11-24 /pmc/articles/PMC2776528/ /pubmed/19956731 http://dx.doi.org/10.1371/journal.pone.0007979 Text en Mavromatis et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Mavromatis, Konstantinos Chu, Ken Ivanova, Natalia Hooper, Sean D. Markowitz, Victor M. Kyrpides, Nikos C. Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System |
title | Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System |
title_full | Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System |
title_fullStr | Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System |
title_full_unstemmed | Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System |
title_short | Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System |
title_sort | gene context analysis in the integrated microbial genomes (img) data management system |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2776528/ https://www.ncbi.nlm.nih.gov/pubmed/19956731 http://dx.doi.org/10.1371/journal.pone.0007979 |
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