The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants.
Identifieur interne : 000290 ( Main/Curation ); précédent : 000289; suivant : 000291The GMOseek matrix: a decision support tool for optimizing the detection of genetically modified plants.
Auteurs : Annette Block [Belgique] ; Frédéric Debode ; Lutz Grohmann ; Julie Hulin ; Isabel Taverniers ; Linda Kluga ; Elodie Barbau-Piednoir ; Sylvia Broeders ; Ingrid Huber ; Marc Van Den Bulcke ; Petra Heinze ; Gilbert Berben ; Ulrich Busch ; Nancy Roosens ; Eric Janssen ; Jana Žel ; Kristina Gruden ; Dany MorissetSource :
- BMC bioinformatics [ 1471-2105 ] ; 2013.
English descriptors
- KwdEn :
- MESH :
- genetics : Plants, Genetically Modified.
- methods : Polymerase Chain Reaction.
- Base Sequence, Decision Support Techniques, Genes, Plant.
Abstract
Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs' molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information.
DOI: 10.1186/1471-2105-14-256
PubMed: 23965170
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pubmed:23965170Le document en format XML
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<series><title level="j">BMC bioinformatics</title>
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<front><div type="abstract" xml:lang="en">Since their first commercialization, the diversity of taxa and the genetic composition of transgene sequences in genetically modified plants (GMOs) are constantly increasing. To date, the detection of GMOs and derived products is commonly performed by PCR-based methods targeting specific DNA sequences introduced into the host genome. Information available regarding the GMOs' molecular characterization is dispersed and not appropriately organized. For this reason, GMO testing is very challenging and requires more complex screening strategies and decision making schemes, demanding in return the use of efficient bioinformatics tools relying on reliable information.</div>
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