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GmoDetector: An accurate and efficient GMO identification approach and its applications
Lihong Chen1, Junfei Zhou1, Tiantian Li1
1Institute for Systems Biology, Jianghan University, Wuhan, Hubei 430056, PR China.
Abstract:
The rapid increase of genetically modified organisms (GMOs) entering the food and feed markets, and the contamination of donor (micro)organisms of transgenic elements make it more challenging for the existing GMO detection. In this study, we developed a high-throughput and contamination-removal GMO detection approach named as GmoDetector. GmoDetector targeted 64 common transgenic elements and 76 GMO-specific events collected from 251 singular GM events, and combined with next generation sequencing (NGS) and target enrichment technology to detect various GMOs. As a result, GmoDetector was able to exclude the donor (micro)organism contamination, and detect the authorized and unauthorized GMOs (UGMOs) in any forms of food or feed, such as processed or unprocessed. The sensitivity of GmoDetector is as low as 0.1% (GMO content), which has met the GMO labeling threshold for all countries. Therefore, GmoDetector is a robust tool for accurate and efficient detection of the authorized and UGMOs.
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