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

Sander Willems UGent, Marie-Alice Fraiture, Dieter Deforce UGent, Sigrid De Keersmaecker, Philippe Herman, Marc De Loose UGent, Tom Ruttink, Filip Van Nieuwerburgh UGent and Nancy Roosens (2016) FOOD CHEMISTRY. 192. p.788-798
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.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
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
journal title
FOOD CHEMISTRY
Food Chem.
volume
192
pages
788 - 798
Web of Science type
Article
Web of Science id
000362304500100
JCR category
FOOD SCIENCE & TECHNOLOGY
JCR impact factor
4.529 (2016)
JCR rank
6/129 (2016)
JCR quartile
1 (2016)
ISSN
0308-8146
DOI
10.1016/j.foodchem.2015.07.074
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
6842102
handle
http://hdl.handle.net/1854/LU-6842102
date created
2015-06-23 08:44:54
date last changed
2017-06-12 10:18:53
@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},
}

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.