کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7590312 | 1492099 | 2016 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Statistical framework for detection of genetically modified organisms based on Next Generation Sequencing
ترجمه فارسی عنوان
چارچوب آماری برای شناسایی ارگانیزم های اصلاح شده ژنتیکی براساس توالی نسل بعدی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Food Chemistry - Volume 192, 1 February 2016, Pages 788-798
Journal: Food Chemistry - Volume 192, 1 February 2016, Pages 788-798
نویسندگان
Sander Willems, Marie-Alice Fraiture, Dieter Deforce, Sigrid C.J. De Keersmaecker, Marc De Loose, Tom Ruttink, Philippe Herman, Filip Van Nieuwerburgh, Nancy Roosens,