Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing.
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.
['Willems|Sander|S|', 'Fraiture|Marie-Alice|MA|', 'Deforce|Dieter|D|', 'De Keersmaecker|Sigrid C J|SC|', 'De Loose|Marc|M|', 'Ruttink|Tom|T|', 'Herman|Philippe|P|', 'Van Nieuwerburgh|Filip|F|', 'Roosens|Nancy|N|']
D001483:Base Sequence D018556:Crops, Agricultural D018744:DNA, Plant D005240:Feasibility Studies D015723:Gene Library D017343:Genes, Plant D059014:High-Throughput Nucleotide Sequencing D008969:Molecular Sequence Data D012275:Oryza D030821:Plants, Genetically Modified D060888:Real-Time Polymerase Chain Reaction D015203:Reproducibility of Results D016415:Sequence Alignment D017422:Sequence Analysis, DNA D019076:Transgenes
Statistical framework NGS GM rice Bioinformatics GMO detection Processed food