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Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing

(2016) FOOD CHEMISTRY. 192. p.788-798
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Abstract
Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgeneevent in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops.
Keywords
NGS, GMO detection, Bioinformatics, GM rice, Statistical framework, Processed food, REAL-TIME PCR, UNAUTHORIZED GMOS, MODIFIED PLANTS, FOOD-PRODUCTS, GENOME SIZE, DNA WALKING, VALIDATION, MITOCHONDRIA, INTRAGENESIS, CISGENESIS

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Chicago
Willems, Sander, Marie-Alice Fraiture, Dieter Deforce, Sigrid De Keersmaecker, Philippe Herman, Marc De Loose, Tom Ruttink, Filip Van Nieuwerburgh, and Nancy Roosens. 2016. “Statistical Framework for Detection of Genetically Modified Organisms Based on Next Generation Sequencing.” Food Chemistry 192: 788–798.
APA
Willems, Sander, Fraiture, M.-A., Deforce, D., De Keersmaecker, S., Herman, P., De Loose, M., Ruttink, T., et al. (2016). Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing. FOOD CHEMISTRY, 192, 788–798.
Vancouver
1.
Willems S, Fraiture M-A, Deforce D, De Keersmaecker S, Herman P, De Loose M, et al. Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing. FOOD CHEMISTRY. 2016;192:788–98.
MLA
Willems, Sander, Marie-Alice Fraiture, Dieter Deforce, et al. “Statistical Framework for Detection of Genetically Modified Organisms Based on Next Generation Sequencing.” FOOD CHEMISTRY 192 (2016): 788–798. Print.
@article{6842102,
  abstract     = {Because the number and diversity of genetically modified (GM) crops has significantly increased, their analysis based on real-time PCR (qPCR) methods is becoming increasingly complex and laborious. While several pioneers already investigated Next Generation Sequencing (NGS) as an alternative to qPCR, its practical use has not been assessed for routine analysis. In this study a statistical framework was developed to predict the number of NGS reads needed to detect transgene sequences, to prove their integration into the host genome and to identify the specific transgeneevent in a sample with known composition. This framework was validated by applying it to experimental data from food matrices composed of pure GM rice, processed GM rice (noodles) or a 10\% GM/non-GM rice mixture, revealing some influential factors. Finally, feasibility of NGS for routine analysis of GM crops was investigated by applying the framework to samples commonly encountered in routine analysis of GM crops.},
  author       = {Willems, Sander and Fraiture, Marie-Alice and Deforce, Dieter and De Keersmaecker, Sigrid and Herman, Philippe and De Loose, Marc and Ruttink, Tom and Van Nieuwerburgh, Filip and Roosens, Nancy},
  issn         = {0308-8146},
  journal      = {FOOD CHEMISTRY},
  keyword      = {NGS,GMO detection,Bioinformatics,GM rice,Statistical framework,Processed food,REAL-TIME PCR,UNAUTHORIZED GMOS,MODIFIED PLANTS,FOOD-PRODUCTS,GENOME SIZE,DNA WALKING,VALIDATION,MITOCHONDRIA,INTRAGENESIS,CISGENESIS},
  language     = {eng},
  pages        = {788--798},
  title        = {Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing},
  url          = {http://dx.doi.org/10.1016/j.foodchem.2015.07.074},
  volume       = {192},
  year         = {2016},
}

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