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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...

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Autores principales: Mavromatis, Konstantinos, Chu, Ken, Ivanova, Natalia, Hooper, Sean D., Markowitz, Victor M., Kyrpides, Nikos C.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
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.
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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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