JRC GMO-Matrix: a web application to support Genetically Modified Organisms detection strategies.
BMC Bioinformatics
; 15: 417, 2014 Dec 30.
Article
em En
| MEDLINE
| ID: mdl-25547877
BACKGROUND: The polymerase chain reaction (PCR) is the current state of the art technique for DNA-based detection of Genetically Modified Organisms (GMOs). A typical control strategy starts by analyzing a sample for the presence of target sequences (GM-elements) known to be present in many GMOs. Positive findings from this "screening" are then confirmed with GM (event) specific test methods. A reliable knowledge of which GMOs are detected by combinations of GM-detection methods is thus crucial to minimize the verification efforts. DESCRIPTION: In this article, we describe a novel platform that links the information of two unique databases built and maintained by the European Union Reference Laboratory for Genetically Modified Food and Feed (EU-RL GMFF) at the Joint Research Centre (JRC) of the European Commission, one containing the sequence information of known GM-events and the other validated PCR-based detection and identification methods. The new platform compiles in silico determinations of the detection of a wide range of GMOs by the available detection methods using existing scripts that simulate PCR amplification and, when present, probe binding. The correctness of the information has been verified by comparing the in silico conclusions to experimental results for a subset of forty-nine GM events and six methods. CONCLUSIONS: The JRC GMO-Matrix is unique for its reliance on DNA sequence data and its flexibility in integrating novel GMOs and new detection methods. Users can mine the database using a set of web interfaces that thus provide a valuable support to GMO control laboratories in planning and evaluating their GMO screening strategies. The platform is accessible at http://gmo-crl.jrc.ec.europa.eu/jrcgmomatrix/ .
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MEDLINE
Assunto principal:
Software
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Técnicas de Apoio para a Decisão
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Plantas Geneticamente Modificadas
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Genes de Plantas
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DNA de Plantas
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Biologia Computacional
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article