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Systematic identification and analysis of frequent gene fusion events in metabolic pathways
BACKGROUND: Gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availabili...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4921024/ https://www.ncbi.nlm.nih.gov/pubmed/27342196 http://dx.doi.org/10.1186/s12864-016-2782-3 |
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author | Henry, Christopher S. Lerma-Ortiz, Claudia Gerdes, Svetlana Y. Mullen, Jeffrey D. Colasanti, Ric Zhukov, Aleksey Frelin, Océane Thiaville, Jennifer J. Zallot, Rémi Niehaus, Thomas D. Hasnain, Ghulam Conrad, Neal Hanson, Andrew D. de Crécy-Lagard, Valérie |
author_facet | Henry, Christopher S. Lerma-Ortiz, Claudia Gerdes, Svetlana Y. Mullen, Jeffrey D. Colasanti, Ric Zhukov, Aleksey Frelin, Océane Thiaville, Jennifer J. Zallot, Rémi Niehaus, Thomas D. Hasnain, Ghulam Conrad, Neal Hanson, Andrew D. de Crécy-Lagard, Valérie |
author_sort | Henry, Christopher S. |
collection | PubMed |
description | BACKGROUND: Gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur. RESULTS: Here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database at http://modelseed.org/projects/fusions/. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent. CONCLUSIONS: Customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2782-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4921024 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-49210242016-06-26 Systematic identification and analysis of frequent gene fusion events in metabolic pathways Henry, Christopher S. Lerma-Ortiz, Claudia Gerdes, Svetlana Y. Mullen, Jeffrey D. Colasanti, Ric Zhukov, Aleksey Frelin, Océane Thiaville, Jennifer J. Zallot, Rémi Niehaus, Thomas D. Hasnain, Ghulam Conrad, Neal Hanson, Andrew D. de Crécy-Lagard, Valérie BMC Genomics Research Article BACKGROUND: Gene fusions are the most powerful type of in silico-derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur. RESULTS: Here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database at http://modelseed.org/projects/fusions/. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent. CONCLUSIONS: Customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-016-2782-3) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-24 /pmc/articles/PMC4921024/ /pubmed/27342196 http://dx.doi.org/10.1186/s12864-016-2782-3 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Henry, Christopher S. Lerma-Ortiz, Claudia Gerdes, Svetlana Y. Mullen, Jeffrey D. Colasanti, Ric Zhukov, Aleksey Frelin, Océane Thiaville, Jennifer J. Zallot, Rémi Niehaus, Thomas D. Hasnain, Ghulam Conrad, Neal Hanson, Andrew D. de Crécy-Lagard, Valérie Systematic identification and analysis of frequent gene fusion events in metabolic pathways |
title | Systematic identification and analysis of frequent gene fusion events in metabolic pathways |
title_full | Systematic identification and analysis of frequent gene fusion events in metabolic pathways |
title_fullStr | Systematic identification and analysis of frequent gene fusion events in metabolic pathways |
title_full_unstemmed | Systematic identification and analysis of frequent gene fusion events in metabolic pathways |
title_short | Systematic identification and analysis of frequent gene fusion events in metabolic pathways |
title_sort | systematic identification and analysis of frequent gene fusion events in metabolic pathways |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4921024/ https://www.ncbi.nlm.nih.gov/pubmed/27342196 http://dx.doi.org/10.1186/s12864-016-2782-3 |
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