TY - JOUR T1 - Statistical framework for detection of Genetically Modified Organisms based on Next Generation Sequencing JF - Food Chemistry Y1 - 2016 A1 - Willems, Sander A1 - Marie-Alice Fraiture A1 - Deforce, Dieter A1 - Sigrid C.J. De Keersmaecker A1 - Philippe Herman A1 - De Loose, Marc A1 - Ruttink, Tom A1 - Van Nieuwerburgh, Filip A1 - Nancy Roosens KW - bioinformatics KW - detection KW - GM rice KW - GMO KW - NGS KW - Processed food KW - Statistical framework AB -

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 transgene event 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.

VL - 192 CP - 1 M3 - https://doi.org/10.1016/j.foodchem.2015.07.074 ER -