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Recently, we developed a DNA walking system for the detection and characterization of a broad spectrum of GMOs in routine analysis of food/feed matrices. Here, we present a new version with improved throughput and sensitivity by coupling the DNA walking system to Pacific Bioscience® Next-generation sequencing technology. The performance of the new strategy was thoroughly assessed through several assays. First, we tested its detection and identification capability on grains with high or low GMO content. Second, the potential impacts of food processing were investigated using rice noodle samples. Finally, GMO mixtures and a real-life sample were analyzed to illustrate the applicability of the proposed strategy in routine GMO analysis. In all tested samples, the presence of multiple GMOs was unambiguously proven by the characterization of transgene flanking regions and the combinations of elements that are typical for transgene constructs. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]