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Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction
BACKGROUND: When generating a genetically modified organism (GMO), the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods...
Autores principales: | , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2764706/ https://www.ncbi.nlm.nih.gov/pubmed/19814792 http://dx.doi.org/10.1186/1472-6750-9-87 |
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author | Tengs, Torstein Zhang, Haibo Holst-Jensen, Arne Bohlin, Jon Butenko, Melinka A Kristoffersen, Anja Bråthen Sorteberg, Hilde-Gunn Opsahl Berdal, Knut G |
author_facet | Tengs, Torstein Zhang, Haibo Holst-Jensen, Arne Bohlin, Jon Butenko, Melinka A Kristoffersen, Anja Bråthen Sorteberg, Hilde-Gunn Opsahl Berdal, Knut G |
author_sort | Tengs, Torstein |
collection | PubMed |
description | BACKGROUND: When generating a genetically modified organism (GMO), the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods that are reliably able to determine if an organism has been genetically altered if the nature of the modification is unknown. RESULTS: We show that the concept of computational subtraction can be used to identify transgenic cDNA sequences from genetically modified plants. Our datasets include 454-type sequences from a transgenic line of Arabidopsis thaliana and published EST datasets from commercially relevant species (rice and papaya). CONCLUSION: We believe that computational subtraction represents a powerful new strategy for determining if an organism has been genetically modified as well as to define the nature of the modification. Fewer assumptions have to be made compared to methods currently in use and this is an advantage particularly when working with unknown GMOs. |
format | Text |
id | pubmed-2764706 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-27647062009-10-21 Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction Tengs, Torstein Zhang, Haibo Holst-Jensen, Arne Bohlin, Jon Butenko, Melinka A Kristoffersen, Anja Bråthen Sorteberg, Hilde-Gunn Opsahl Berdal, Knut G BMC Biotechnol Methodology Article BACKGROUND: When generating a genetically modified organism (GMO), the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods that are reliably able to determine if an organism has been genetically altered if the nature of the modification is unknown. RESULTS: We show that the concept of computational subtraction can be used to identify transgenic cDNA sequences from genetically modified plants. Our datasets include 454-type sequences from a transgenic line of Arabidopsis thaliana and published EST datasets from commercially relevant species (rice and papaya). CONCLUSION: We believe that computational subtraction represents a powerful new strategy for determining if an organism has been genetically modified as well as to define the nature of the modification. Fewer assumptions have to be made compared to methods currently in use and this is an advantage particularly when working with unknown GMOs. BioMed Central 2009-10-08 /pmc/articles/PMC2764706/ /pubmed/19814792 http://dx.doi.org/10.1186/1472-6750-9-87 Text en Copyright © 2009 Tengs et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Tengs, Torstein Zhang, Haibo Holst-Jensen, Arne Bohlin, Jon Butenko, Melinka A Kristoffersen, Anja Bråthen Sorteberg, Hilde-Gunn Opsahl Berdal, Knut G Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction |
title | Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction |
title_full | Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction |
title_fullStr | Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction |
title_full_unstemmed | Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction |
title_short | Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction |
title_sort | characterization of unknown genetic modifications using high throughput sequencing and computational subtraction |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2764706/ https://www.ncbi.nlm.nih.gov/pubmed/19814792 http://dx.doi.org/10.1186/1472-6750-9-87 |
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